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DeepSpeech

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D
DeepSpeech
“7cc864ea4206cc67c9f38f9e55e83edafa5221d2”上不存在“mobile/src/operators/kernel/gru_unit_kernel.h”
未验证 提交 43af22e4 编写于 作者: Z Zhao Yuting 提交者： GitHub
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Merge branch 'PaddlePaddle:develop' into test

上级 3e7e247a ea9ee937
展开全部 隐藏空白更改
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Showing with 2682 addition and 980 deletion
README.md
浏览文件 @ 43af22e4
...@@ -613,7 +613,7 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r ...@@ -613,7 +613,7 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
</td> </td>
</tr> </tr>
<tr> <tr>
<td rowspan="3">Voice Cloning</td> <td rowspan="4">Voice Cloning</td>
<td>GE2E</td> <td>GE2E</td>
<td >Librispeech, etc.</td> <td >Librispeech, etc.</td>
<td> <td>
...@@ -633,13 +633,20 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r ...@@ -633,13 +633,20 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
<td> <td>
<a href = "./examples/aishell3/vc1">ge2e-fastspeech2-aishell3</a> <a href = "./examples/aishell3/vc1">ge2e-fastspeech2-aishell3</a>
</td> </td>
</tr>
<tr>
<td>GE2E + VITS</td>
<td>AISHELL-3</td>
<td>
<a href = "./examples/aishell3/vits-vc">ge2e-vits-aishell3</a>
</td>
</tr> </tr>
<tr> <tr>
<td rowspan="3">End-to-End</td> <td rowspan="3">End-to-End</td>
<td>VITS</td> <td>VITS</td>
<td >CSMSC</td> <td>CSMSC / AISHELL-3</td>
<td> <td>
<a href = "./examples/csmsc/vits">VITS-csmsc</a> <a href = "./examples/csmsc/vits">VITS-csmsc</a> / <a href = "./examples/aishell3/vits">VITS-aishell3</a>
</td> </td>
</tr> </tr>
</tbody> </tbody>
......
README_cn.md
浏览文件 @ 43af22e4
...@@ -608,7 +608,7 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声 ...@@ -608,7 +608,7 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声
</td> </td>
</tr> </tr>
<tr> <tr>
<td rowspan="3">声音克隆</td> <td rowspan="4">声音克隆</td>
<td>GE2E</td> <td>GE2E</td>
<td >Librispeech, etc.</td> <td >Librispeech, etc.</td>
<td> <td>
...@@ -629,13 +629,20 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声 ...@@ -629,13 +629,20 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声
<a href = "./examples/aishell3/vc1">ge2e-fastspeech2-aishell3</a> <a href = "./examples/aishell3/vc1">ge2e-fastspeech2-aishell3</a>
</td> </td>
</tr> </tr>
<tr>
<td>GE2E + VITS</td>
<td>AISHELL-3</td>
<td>
<a href = "./examples/aishell3/vits-vc">ge2e-vits-aishell3</a>
</td>
</tr>
</tr> </tr>
<tr> <tr>
<td rowspan="3">端到端</td> <td rowspan="3">端到端</td>
<td>VITS</td> <td>VITS</td>
<td >CSMSC</td> <td>CSMSC / AISHELL-3</td>
<td> <td>
<a href = "./examples/csmsc/vits">VITS-csmsc</a> <a href = "./examples/csmsc/vits">VITS-csmsc</a> / <a href = "./examples/aishell3/vits">VITS-aishell3</a>
</td> </td>
</tr> </tr>
</tbody> </tbody>
......
demos/audio_searching/src/operations/load.py
浏览文件 @ 43af22e4
...@@ -26,8 +26,9 @@ def get_audios(path): ...@@ -26,8 +26,9 @@ def get_audios(path):
""" """
supported_formats = [".wav", ".mp3", ".ogg", ".flac", ".m4a"] supported_formats = [".wav", ".mp3", ".ogg", ".flac", ".m4a"]
return [ return [
item for sublist in [[os.path.join(dir, file) for file in files] item
for dir, _, files in list(os.walk(path))] for sublist in [[os.path.join(dir, file) for file in files]
for dir, _, files in list(os.walk(path))]
for item in sublist if os.path.splitext(item)[1] in supported_formats for item in sublist if os.path.splitext(item)[1] in supported_formats
] ]
......
demos/speech_web/API.md
浏览文件 @ 43af22e4
...@@ -401,4 +401,4 @@ curl -X 'GET' \ ...@@ -401,4 +401,4 @@ curl -X 'GET' \
"code": 0, "code": 0,
"result":"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA", "result":"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA",
"message": "ok" "message": "ok"
``` ```
\ No newline at end of file
demos/speech_web/speech_server/main.py
浏览文件 @ 43af22e4
...@@ -3,48 +3,48 @@ ...@@ -3,48 +3,48 @@
# 2. 接收录音音频，返回识别结果 # 2. 接收录音音频，返回识别结果
# 3. 接收ASR识别结果，返回NLP对话结果 # 3. 接收ASR识别结果，返回NLP对话结果
# 4. 接收NLP对话结果，返回TTS音频 # 4. 接收NLP对话结果，返回TTS音频
import argparse
import base64 import base64
import yaml
import os
import json
import datetime import datetime
import json
import os
from typing import List
import aiofiles
import librosa import librosa
import soundfile as sf import soundfile as sf
import numpy as np
import argparse
import uvicorn import uvicorn
import aiofiles from fastapi import FastAPI
from typing import Optional, List from fastapi import File
from pydantic import BaseModel from fastapi import Form
from fastapi import FastAPI, Header, File, UploadFile, Form, Cookie, WebSocket, WebSocketDisconnect from fastapi import UploadFile
from fastapi import WebSocket
from fastapi import WebSocketDisconnect
from fastapi.responses import StreamingResponse from fastapi.responses import StreamingResponse
from starlette.responses import FileResponse from pydantic import BaseModel
from starlette.middleware.cors import CORSMiddleware
from starlette.requests import Request
from starlette.websockets import WebSocketState as WebSocketState
from src.AudioManeger import AudioMannger from src.AudioManeger import AudioMannger
from src.util import *
from src.robot import Robot from src.robot import Robot
from src.WebsocketManeger import ConnectionManager
from src.SpeechBase.vpr import VPR from src.SpeechBase.vpr import VPR
from src.util import *
from src.WebsocketManeger import ConnectionManager
from starlette.middleware.cors import CORSMiddleware
from starlette.requests import Request
from starlette.responses import FileResponse
from starlette.websockets import WebSocketState as WebSocketState
from paddlespeech.server.engine.asr.online.python.asr_engine import PaddleASRConnectionHanddler from paddlespeech.server.engine.asr.online.python.asr_engine import PaddleASRConnectionHanddler
from paddlespeech.server.utils.audio_process import float2pcm from paddlespeech.server.utils.audio_process import float2pcm
# 解析配置 # 解析配置
parser = argparse.ArgumentParser( parser = argparse.ArgumentParser(prog='PaddleSpeechDemo', add_help=True)
prog='PaddleSpeechDemo', add_help=True)
parser.add_argument( parser.add_argument(
"--port", "--port",
action="store", action="store",
type=int, type=int,
help="port of the app", help="port of the app",
default=8010, default=8010,
required=False) required=False)
args = parser.parse_args() args = parser.parse_args()
port = args.port port = args.port
...@@ -60,39 +60,41 @@ ie_model_path = "source/model" ...@@ -60,39 +60,41 @@ ie_model_path = "source/model"
UPLOAD_PATH = "source/vpr" UPLOAD_PATH = "source/vpr"
WAV_PATH = "source/wav" WAV_PATH = "source/wav"
base_sources = [UPLOAD_PATH, WAV_PATH]
base_sources = [
UPLOAD_PATH, WAV_PATH
]
for path in base_sources: for path in base_sources:
os.makedirs(path, exist_ok=True) os.makedirs(path, exist_ok=True)
# 初始化 # 初始化
app = FastAPI() app = FastAPI()
chatbot = Robot(asr_config, tts_config, asr_init_path, ie_model_path=ie_model_path) chatbot = Robot(
asr_config, tts_config, asr_init_path, ie_model_path=ie_model_path)
manager = ConnectionManager() manager = ConnectionManager()
aumanager = AudioMannger(chatbot) aumanager = AudioMannger(chatbot)
aumanager.init() aumanager.init()
vpr = VPR(db_path, dim = 192, top_k = 5) vpr = VPR(db_path, dim=192, top_k=5)
# 服务配置 # 服务配置
class NlpBase(BaseModel): class NlpBase(BaseModel):
chat: str chat: str
class TtsBase(BaseModel): class TtsBase(BaseModel):
text: str text: str
class Audios: class Audios:
def __init__(self) -> None: def __init__(self) -> None:
self.audios = b"" self.audios = b""
audios = Audios() audios = Audios()
###################################################################### ######################################################################
########################### ASR 服务 ################################# ########################### ASR 服务 #################################
##################################################################### #####################################################################
# 接收文件，返回ASR结果 # 接收文件，返回ASR结果
# 上传文件 # 上传文件
@app.post("/asr/offline") @app.post("/asr/offline")
...@@ -101,7 +103,8 @@ async def speech2textOffline(files: List[UploadFile]): ...@@ -101,7 +103,8 @@ async def speech2textOffline(files: List[UploadFile]):
asr_res = "" asr_res = ""
for file in files[:1]: for file in files[:1]:
# 生成时间戳 # 生成时间戳
now_name = "asr_offline_" + datetime.datetime.strftime(datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav" now_name = "asr_offline_" + datetime.datetime.strftime(
datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav"
out_file_path = os.path.join(WAV_PATH, now_name) out_file_path = os.path.join(WAV_PATH, now_name)
async with aiofiles.open(out_file_path, 'wb') as out_file: async with aiofiles.open(out_file_path, 'wb') as out_file:
content = await file.read() # async read content = await file.read() # async read
...@@ -110,10 +113,9 @@ async def speech2textOffline(files: List[UploadFile]): ...@@ -110,10 +113,9 @@ async def speech2textOffline(files: List[UploadFile]):
# 返回ASR识别结果 # 返回ASR识别结果
asr_res = chatbot.speech2text(out_file_path) asr_res = chatbot.speech2text(out_file_path)
return SuccessRequest(result=asr_res) return SuccessRequest(result=asr_res)
# else:
# return ErrorRequest(message="文件不是.wav格式")
return ErrorRequest(message="上传文件为空") return ErrorRequest(message="上传文件为空")
# 接收文件，同时将wav强制转成16k, int16类型 # 接收文件，同时将wav强制转成16k, int16类型
@app.post("/asr/offlinefile") @app.post("/asr/offlinefile")
async def speech2textOfflineFile(files: List[UploadFile]): async def speech2textOfflineFile(files: List[UploadFile]):
...@@ -121,7 +123,8 @@ async def speech2textOfflineFile(files: List[UploadFile]): ...@@ -121,7 +123,8 @@ async def speech2textOfflineFile(files: List[UploadFile]):
asr_res = "" asr_res = ""
for file in files[:1]: for file in files[:1]:
# 生成时间戳 # 生成时间戳
now_name = "asr_offline_" + datetime.datetime.strftime(datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav" now_name = "asr_offline_" + datetime.datetime.strftime(
datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav"
out_file_path = os.path.join(WAV_PATH, now_name) out_file_path = os.path.join(WAV_PATH, now_name)
async with aiofiles.open(out_file_path, 'wb') as out_file: async with aiofiles.open(out_file_path, 'wb') as out_file:
content = await file.read() # async read content = await file.read() # async read
...@@ -132,22 +135,18 @@ async def speech2textOfflineFile(files: List[UploadFile]): ...@@ -132,22 +135,18 @@ async def speech2textOfflineFile(files: List[UploadFile]):
wav = float2pcm(wav) # float32 to int16 wav = float2pcm(wav) # float32 to int16
wav_bytes = wav.tobytes() # to bytes wav_bytes = wav.tobytes() # to bytes
wav_base64 = base64.b64encode(wav_bytes).decode('utf8') wav_base64 = base64.b64encode(wav_bytes).decode('utf8')
# 将文件重新写入 # 将文件重新写入
now_name = now_name[:-4] + "_16k" + ".wav" now_name = now_name[:-4] + "_16k" + ".wav"
out_file_path = os.path.join(WAV_PATH, now_name) out_file_path = os.path.join(WAV_PATH, now_name)
sf.write(out_file_path,wav,16000) sf.write(out_file_path, wav, 16000)
# 返回ASR识别结果 # 返回ASR识别结果
asr_res = chatbot.speech2text(out_file_path) asr_res = chatbot.speech2text(out_file_path)
response_res = { response_res = {"asr_result": asr_res, "wav_base64": wav_base64}
"asr_result": asr_res,
"wav_base64": wav_base64
}
return SuccessRequest(result=response_res) return SuccessRequest(result=response_res)
return ErrorRequest(message="上传文件为空")
return ErrorRequest(message="上传文件为空")
# 流式接收测试 # 流式接收测试
...@@ -161,15 +160,17 @@ async def speech2textOnlineRecive(files: List[UploadFile]): ...@@ -161,15 +160,17 @@ async def speech2textOnlineRecive(files: List[UploadFile]):
print(f"audios长度变化: {len(audios.audios)}") print(f"audios长度变化: {len(audios.audios)}")
return SuccessRequest(message="接收成功") return SuccessRequest(message="接收成功")
# 采集环境噪音大小 # 采集环境噪音大小
@app.post("/asr/collectEnv") @app.post("/asr/collectEnv")
async def collectEnv(files: List[UploadFile]): async def collectEnv(files: List[UploadFile]):
for file in files[:1]: for file in files[:1]:
content = await file.read() # async read content = await file.read() # async read
# 初始化, wav 前44字节是头部信息 # 初始化, wav 前44字节是头部信息
aumanager.compute_env_volume(content[44:]) aumanager.compute_env_volume(content[44:])
vad_ = aumanager.vad_threshold vad_ = aumanager.vad_threshold
return SuccessRequest(result=vad_,message="采集环境噪音成功") return SuccessRequest(result=vad_, message="采集环境噪音成功")
# 停止录音 # 停止录音
@app.get("/asr/stopRecord") @app.get("/asr/stopRecord")
...@@ -179,6 +180,7 @@ async def stopRecord(): ...@@ -179,6 +180,7 @@ async def stopRecord():
print("Online录音暂停") print("Online录音暂停")
return SuccessRequest(message="停止成功") return SuccessRequest(message="停止成功")
# 恢复录音 # 恢复录音
@app.get("/asr/resumeRecord") @app.get("/asr/resumeRecord")
async def resumeRecord(): async def resumeRecord():
...@@ -187,7 +189,7 @@ async def resumeRecord(): ...@@ -187,7 +189,7 @@ async def resumeRecord():
return SuccessRequest(message="Online录音恢复") return SuccessRequest(message="Online录音恢复")
# 聊天用的ASR # 聊天用的 ASR
@app.websocket("/ws/asr/offlineStream") @app.websocket("/ws/asr/offlineStream")
async def websocket_endpoint(websocket: WebSocket): async def websocket_endpoint(websocket: WebSocket):
await manager.connect(websocket) await manager.connect(websocket)
...@@ -210,9 +212,9 @@ async def websocket_endpoint(websocket: WebSocket): ...@@ -210,9 +212,9 @@ async def websocket_endpoint(websocket: WebSocket):
# print(f"用户-{user}-离开") # print(f"用户-{user}-离开")
# Online识别的ASR # 流式识别的 ASR
@app.websocket('/ws/asr/onlineStream') @app.websocket('/ws/asr/onlineStream')
async def websocket_endpoint(websocket: WebSocket): async def websocket_endpoint_online(websocket: WebSocket):
"""PaddleSpeech Online ASR Server api """PaddleSpeech Online ASR Server api
Args: Args:
...@@ -298,12 +300,14 @@ async def websocket_endpoint(websocket: WebSocket): ...@@ -298,12 +300,14 @@ async def websocket_endpoint(websocket: WebSocket):
except WebSocketDisconnect: except WebSocketDisconnect:
pass pass
###################################################################### ######################################################################
########################### NLP 服务 ################################# ########################### NLP 服务 #################################
##################################################################### #####################################################################
@app.post("/nlp/chat") @app.post("/nlp/chat")
async def chatOffline(nlp_base:NlpBase): async def chatOffline(nlp_base: NlpBase):
chat = nlp_base.chat chat = nlp_base.chat
if not chat: if not chat:
return ErrorRequest(message="传入文本为空") return ErrorRequest(message="传入文本为空")
...@@ -311,8 +315,9 @@ async def chatOffline(nlp_base:NlpBase): ...@@ -311,8 +315,9 @@ async def chatOffline(nlp_base:NlpBase):
res = chatbot.chat(chat) res = chatbot.chat(chat)
return SuccessRequest(result=res) return SuccessRequest(result=res)
@app.post("/nlp/ie") @app.post("/nlp/ie")
async def ieOffline(nlp_base:NlpBase): async def ieOffline(nlp_base: NlpBase):
nlp_text = nlp_base.chat nlp_text = nlp_base.chat
if not nlp_text: if not nlp_text:
return ErrorRequest(message="传入文本为空") return ErrorRequest(message="传入文本为空")
...@@ -320,17 +325,20 @@ async def ieOffline(nlp_base:NlpBase): ...@@ -320,17 +325,20 @@ async def ieOffline(nlp_base:NlpBase):
res = chatbot.ie(nlp_text) res = chatbot.ie(nlp_text)
return SuccessRequest(result=res) return SuccessRequest(result=res)
###################################################################### ######################################################################
########################### TTS 服务 ################################# ########################### TTS 服务 #################################
##################################################################### #####################################################################
@app.post("/tts/offline") @app.post("/tts/offline")
async def text2speechOffline(tts_base:TtsBase): async def text2speechOffline(tts_base: TtsBase):
text = tts_base.text text = tts_base.text
if not text: if not text:
return ErrorRequest(message="文本为空") return ErrorRequest(message="文本为空")
else: else:
now_name = "tts_"+ datetime.datetime.strftime(datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav" now_name = "tts_" + datetime.datetime.strftime(
datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav"
out_file_path = os.path.join(WAV_PATH, now_name) out_file_path = os.path.join(WAV_PATH, now_name)
# 保存为文件，再转成base64传输 # 保存为文件，再转成base64传输
chatbot.text2speech(text, outpath=out_file_path) chatbot.text2speech(text, outpath=out_file_path)
...@@ -339,12 +347,14 @@ async def text2speechOffline(tts_base:TtsBase): ...@@ -339,12 +347,14 @@ async def text2speechOffline(tts_base:TtsBase):
base_str = base64.b64encode(data_bin) base_str = base64.b64encode(data_bin)
return SuccessRequest(result=base_str) return SuccessRequest(result=base_str)
# http流式TTS # http流式TTS
@app.post("/tts/online") @app.post("/tts/online")
async def stream_tts(request_body: TtsBase): async def stream_tts(request_body: TtsBase):
text = request_body.text text = request_body.text
return StreamingResponse(chatbot.text2speechStreamBytes(text=text)) return StreamingResponse(chatbot.text2speechStreamBytes(text=text))
# ws流式TTS # ws流式TTS
@app.websocket("/ws/tts/online") @app.websocket("/ws/tts/online")
async def stream_ttsWS(websocket: WebSocket): async def stream_ttsWS(websocket: WebSocket):
...@@ -356,17 +366,11 @@ async def stream_ttsWS(websocket: WebSocket): ...@@ -356,17 +366,11 @@ async def stream_ttsWS(websocket: WebSocket):
if text: if text:
for sub_wav in chatbot.text2speechStream(text=text): for sub_wav in chatbot.text2speechStream(text=text):
# print("发送sub wav: ", len(sub_wav)) # print("发送sub wav: ", len(sub_wav))
res = { res = {"wav": sub_wav, "done": False}
"wav": sub_wav,
"done": False
}
await websocket.send_json(res) await websocket.send_json(res)
# 输送结束 # 输送结束
res = { res = {"wav": sub_wav, "done": True}
"wav": sub_wav,
"done": True
}
await websocket.send_json(res) await websocket.send_json(res)
# manager.disconnect(websocket) # manager.disconnect(websocket)
...@@ -396,8 +400,9 @@ async def vpr_enroll(table_name: str=None, ...@@ -396,8 +400,9 @@ async def vpr_enroll(table_name: str=None,
return {'status': False, 'msg': "spk_id can not be None"} return {'status': False, 'msg': "spk_id can not be None"}
# Save the upload data to server. # Save the upload data to server.
content = await audio.read() content = await audio.read()
now_name = "vpr_enroll_" + datetime.datetime.strftime(datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav" now_name = "vpr_enroll_" + datetime.datetime.strftime(
audio_path = os.path.join(UPLOAD_PATH, now_name) datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav"
audio_path = os.path.join(UPLOAD_PATH, now_name)
with open(audio_path, "wb+") as f: with open(audio_path, "wb+") as f:
f.write(content) f.write(content)
...@@ -413,20 +418,19 @@ async def vpr_recog(request: Request, ...@@ -413,20 +418,19 @@ async def vpr_recog(request: Request,
audio: UploadFile=File(...)): audio: UploadFile=File(...)):
# Voice print recognition online # Voice print recognition online
# try: # try:
# Save the upload data to server. # Save the upload data to server.
content = await audio.read() content = await audio.read()
now_name = "vpr_query_" + datetime.datetime.strftime(datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav" now_name = "vpr_query_" + datetime.datetime.strftime(
query_audio_path = os.path.join(UPLOAD_PATH, now_name) datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav"
query_audio_path = os.path.join(UPLOAD_PATH, now_name)
with open(query_audio_path, "wb+") as f: with open(query_audio_path, "wb+") as f:
f.write(content) f.write(content)
spk_ids, paths, scores = vpr.do_search_vpr(query_audio_path) spk_ids, paths, scores = vpr.do_search_vpr(query_audio_path)
res = dict(zip(spk_ids, zip(paths, scores))) res = dict(zip(spk_ids, zip(paths, scores)))
# Sort results by distance metric, closest distances first # Sort results by distance metric, closest distances first
res = sorted(res.items(), key=lambda item: item[1][1], reverse=True) res = sorted(res.items(), key=lambda item: item[1][1], reverse=True)
return res return res
# except Exception as e:
# return {'status': False, 'msg': e}, 400
@app.post('/vpr/del') @app.post('/vpr/del')
...@@ -460,17 +464,18 @@ async def vpr_database64(vprId: int): ...@@ -460,17 +464,18 @@ async def vpr_database64(vprId: int):
return {'status': False, 'msg': "vpr_id can not be None"} return {'status': False, 'msg': "vpr_id can not be None"}
audio_path = vpr.do_get_wav(vprId) audio_path = vpr.do_get_wav(vprId)
# 返回base64 # 返回base64
# 将文件转成16k, 16bit类型的wav文件 # 将文件转成16k, 16bit类型的wav文件
wav, sr = librosa.load(audio_path, sr=16000) wav, sr = librosa.load(audio_path, sr=16000)
wav = float2pcm(wav) # float32 to int16 wav = float2pcm(wav) # float32 to int16
wav_bytes = wav.tobytes() # to bytes wav_bytes = wav.tobytes() # to bytes
wav_base64 = base64.b64encode(wav_bytes).decode('utf8') wav_base64 = base64.b64encode(wav_bytes).decode('utf8')
return SuccessRequest(result=wav_base64) return SuccessRequest(result=wav_base64)
except Exception as e: except Exception as e:
return {'status': False, 'msg': e}, 400 return {'status': False, 'msg': e}, 400
@app.get('/vpr/data') @app.get('/vpr/data')
async def vpr_data(vprId: int): async def vpr_data(vprId: int):
# Get the audio file from path by spk_id in MySQL # Get the audio file from path by spk_id in MySQL
...@@ -482,11 +487,6 @@ async def vpr_data(vprId: int): ...@@ -482,11 +487,6 @@ async def vpr_data(vprId: int):
except Exception as e: except Exception as e:
return {'status': False, 'msg': e}, 400 return {'status': False, 'msg': e}, 400
if __name__ == '__main__': if __name__ == '__main__':
uvicorn.run(app=app, host='0.0.0.0', port=port) uvicorn.run(app=app, host='0.0.0.0', port=port)
demos/speech_web/speech_server/requirements.txt
浏览文件 @ 43af22e4
aiofiles aiofiles
faiss-cpu
fastapi fastapi
librosa librosa
numpy numpy
paddlenlp
paddlepaddle
paddlespeech
pydantic pydantic
scikit_learn python-multipartscikit_learn
SoundFile SoundFile
starlette starlette
uvicorn uvicorn
paddlepaddle
paddlespeech
paddlenlp
faiss-cpu
python-multipart
\ No newline at end of file
demos/speech_web/speech_server/src/AudioManeger.py
浏览文件 @ 43af22e4
import imp import datetime
from queue import Queue
import numpy as np
import os import os
import wave import wave
import random
import datetime import numpy as np
from .util import randName from .util import randName
class AudioMannger: class AudioMannger:
def __init__(self, robot, frame_length=160, frame=10, data_width=2, vad_default = 300): def __init__(self,
robot,
frame_length=160,
frame=10,
data_width=2,
vad_default=300):
# 二进制 pcm 流 # 二进制 pcm 流
self.audios = b'' self.audios = b''
self.asr_result = "" self.asr_result = ""
...@@ -20,8 +24,9 @@ class AudioMannger: ...@@ -20,8 +24,9 @@ class AudioMannger:
os.makedirs(self.file_dir, exist_ok=True) os.makedirs(self.file_dir, exist_ok=True)
self.vad_deafult = vad_default self.vad_deafult = vad_default
self.vad_threshold = vad_default self.vad_threshold = vad_default
self.vad_threshold_path = os.path.join(self.file_dir, "vad_threshold.npy") self.vad_threshold_path = os.path.join(self.file_dir,
"vad_threshold.npy")
# 10ms 一帧 # 10ms 一帧
self.frame_length = frame_length self.frame_length = frame_length
# 10帧，检测一次 vad # 10帧，检测一次 vad
...@@ -30,67 +35,64 @@ class AudioMannger: ...@@ -30,67 +35,64 @@ class AudioMannger:
self.data_width = data_width self.data_width = data_width
# window # window
self.window_length = frame_length * frame * data_width self.window_length = frame_length * frame * data_width
# 是否开始录音 # 是否开始录音
self.on_asr = False self.on_asr = False
self.silence_cnt = 0 self.silence_cnt = 0
self.max_silence_cnt = 4 self.max_silence_cnt = 4
self.is_pause = False # 录音暂停与恢复 self.is_pause = False # 录音暂停与恢复
def init(self): def init(self):
if os.path.exists(self.vad_threshold_path): if os.path.exists(self.vad_threshold_path):
# 平均响度文件存在 # 平均响度文件存在
self.vad_threshold = np.load(self.vad_threshold_path) self.vad_threshold = np.load(self.vad_threshold_path)
def clear_audio(self): def clear_audio(self):
# 清空 pcm 累积片段与 asr 识别结果 # 清空 pcm 累积片段与 asr 识别结果
self.audios = b'' self.audios = b''
def clear_asr(self): def clear_asr(self):
self.asr_result = "" self.asr_result = ""
def compute_chunk_volume(self, start_index, pcm_bins): def compute_chunk_volume(self, start_index, pcm_bins):
# 根据帧长计算能量平均值 # 根据帧长计算能量平均值
pcm_bin = pcm_bins[start_index: start_index + self.window_length] pcm_bin = pcm_bins[start_index:start_index + self.window_length]
# 转成 numpy # 转成 numpy
pcm_np = np.frombuffer(pcm_bin, np.int16) pcm_np = np.frombuffer(pcm_bin, np.int16)
# 归一化 + 计算响度 # 归一化 + 计算响度
x = pcm_np.astype(np.float32) x = pcm_np.astype(np.float32)
x = np.abs(x) x = np.abs(x)
return np.mean(x) return np.mean(x)
def is_speech(self, start_index, pcm_bins): def is_speech(self, start_index, pcm_bins):
# 检查是否没 # 检查是否没
if start_index > len(pcm_bins): if start_index > len(pcm_bins):
return False return False
# 检查从这个 start 开始是否为静音帧 # 检查从这个 start 开始是否为静音帧
energy = self.compute_chunk_volume(start_index=start_index, pcm_bins=pcm_bins) energy = self.compute_chunk_volume(
start_index=start_index, pcm_bins=pcm_bins)
# print(energy) # print(energy)
if energy > self.vad_threshold: if energy > self.vad_threshold:
return True return True
else: else:
return False return False
def compute_env_volume(self, pcm_bins): def compute_env_volume(self, pcm_bins):
max_energy = 0 max_energy = 0
start = 0 start = 0
while start < len(pcm_bins): while start < len(pcm_bins):
energy = self.compute_chunk_volume(start_index=start, pcm_bins=pcm_bins) energy = self.compute_chunk_volume(
start_index=start, pcm_bins=pcm_bins)
if energy > max_energy: if energy > max_energy:
max_energy = energy max_energy = energy
start += self.window_length start += self.window_length
self.vad_threshold = max_energy + 100 if max_energy > self.vad_deafult else self.vad_deafult self.vad_threshold = max_energy + 100 if max_energy > self.vad_deafult else self.vad_deafult
# 保存成文件 # 保存成文件
np.save(self.vad_threshold_path, self.vad_threshold) np.save(self.vad_threshold_path, self.vad_threshold)
print(f"vad 阈值大小: {self.vad_threshold}") print(f"vad 阈值大小: {self.vad_threshold}")
print(f"环境采样保存: {os.path.realpath(self.vad_threshold_path)}") print(f"环境采样保存: {os.path.realpath(self.vad_threshold_path)}")
def stream_asr(self, pcm_bin): def stream_asr(self, pcm_bin):
# 先把 pcm_bin 送进去做端点检测 # 先把 pcm_bin 送进去做端点检测
start = 0 start = 0
...@@ -99,7 +101,7 @@ class AudioMannger: ...@@ -99,7 +101,7 @@ class AudioMannger:
self.on_asr = True self.on_asr = True
self.silence_cnt = 0 self.silence_cnt = 0
print("录音中") print("录音中")
self.audios += pcm_bin[ start : start + self.window_length] self.audios += pcm_bin[start:start + self.window_length]
else: else:
if self.on_asr: if self.on_asr:
self.silence_cnt += 1 self.silence_cnt += 1
...@@ -110,41 +112,42 @@ class AudioMannger: ...@@ -110,41 +112,42 @@ class AudioMannger:
print("录音停止") print("录音停止")
# audios 保存为 wav, 送入 ASR # audios 保存为 wav, 送入 ASR
if len(self.audios) > 2 * 16000: if len(self.audios) > 2 * 16000:
file_path = os.path.join(self.file_dir, "asr_" + datetime.datetime.strftime(datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav") file_path = os.path.join(
self.file_dir,
"asr_" + datetime.datetime.strftime(
datetime.datetime.now(),
'%Y%m%d%H%M%S') + randName() + ".wav")
self.save_audio(file_path=file_path) self.save_audio(file_path=file_path)
self.asr_result = self.robot.speech2text(file_path) self.asr_result = self.robot.speech2text(file_path)
self.clear_audio() self.clear_audio()
return self.asr_result return self.asr_result
else: else:
# 正常接收 # 正常接收
print("录音中 静音") print("录音中 静音")
self.audios += pcm_bin[ start : start + self.window_length] self.audios += pcm_bin[start:start + self.window_length]
start += self.window_length start += self.window_length
return "" return ""
def save_audio(self, file_path): def save_audio(self, file_path):
print("保存音频") print("保存音频")
wf = wave.open(file_path, 'wb') # 创建一个音频文件，名字为“01.wav" wf = wave.open(file_path, 'wb') # 创建一个音频文件，名字为“01.wav"
wf.setnchannels(1) # 设置声道数为2 wf.setnchannels(1) # 设置声道数为2
wf.setsampwidth(2) # 设置采样深度为 wf.setsampwidth(2) # 设置采样深度为
wf.setframerate(16000) # 设置采样率为16000 wf.setframerate(16000) # 设置采样率为16000
# 将数据写入创建的音频文件 # 将数据写入创建的音频文件
wf.writeframes(self.audios) wf.writeframes(self.audios)
# 写完后将文件关闭 # 写完后将文件关闭
wf.close() wf.close()
def end(self): def end(self):
# audios 保存为 wav, 送入 ASR # audios 保存为 wav, 送入 ASR
file_path = os.path.join(self.file_dir, "asr.wav") file_path = os.path.join(self.file_dir, "asr.wav")
self.save_audio(file_path=file_path) self.save_audio(file_path=file_path)
return self.robot.speech2text(file_path) return self.robot.speech2text(file_path)
def stop(self): def stop(self):
self.is_pause = True self.is_pause = True
self.audios = b'' self.audios = b''
def resume(self): def resume(self):
self.is_pause = False self.is_pause = False
\ No newline at end of file
demos/speech_web/speech_server/src/SpeechBase/asr.py
浏览文件 @ 43af22e4
from re import sub
import numpy as np import numpy as np
import paddle
import librosa
import soundfile
from paddlespeech.server.engine.asr.online.python.asr_engine import ASREngine from paddlespeech.server.engine.asr.online.python.asr_engine import ASREngine
from paddlespeech.server.engine.asr.online.python.asr_engine import PaddleASRConnectionHanddler from paddlespeech.server.engine.asr.online.python.asr_engine import PaddleASRConnectionHanddler
from paddlespeech.server.utils.config import get_config from paddlespeech.server.utils.config import get_config
def readWave(samples): def readWave(samples):
x_len = len(samples) x_len = len(samples)
...@@ -31,20 +28,23 @@ def readWave(samples): ...@@ -31,20 +28,23 @@ def readWave(samples):
class ASR: class ASR:
def __init__(self, config_path, ) -> None: def __init__(
self,
config_path, ) -> None:
self.config = get_config(config_path)['asr_online'] self.config = get_config(config_path)['asr_online']
self.engine = ASREngine() self.engine = ASREngine()
self.engine.init(self.config) self.engine.init(self.config)
self.connection_handler = PaddleASRConnectionHanddler(self.engine) self.connection_handler = PaddleASRConnectionHanddler(self.engine)
def offlineASR(self, samples, sample_rate=16000): def offlineASR(self, samples, sample_rate=16000):
x_chunk, x_chunk_lens = self.engine.preprocess(samples=samples, sample_rate=sample_rate) x_chunk, x_chunk_lens = self.engine.preprocess(
samples=samples, sample_rate=sample_rate)
self.engine.run(x_chunk, x_chunk_lens) self.engine.run(x_chunk, x_chunk_lens)
result = self.engine.postprocess() result = self.engine.postprocess()
self.engine.reset() self.engine.reset()
return result return result
def onlineASR(self, samples:bytes=None, is_finished=False): def onlineASR(self, samples: bytes=None, is_finished=False):
if not is_finished: if not is_finished:
# 流式开始 # 流式开始
self.connection_handler.extract_feat(samples) self.connection_handler.extract_feat(samples)
...@@ -58,5 +58,3 @@ class ASR: ...@@ -58,5 +58,3 @@ class ASR:
asr_results = self.connection_handler.get_result() asr_results = self.connection_handler.get_result()
self.connection_handler.reset() self.connection_handler.reset()
return asr_results return asr_results
\ No newline at end of file
demos/speech_web/speech_server/src/SpeechBase/nlp.py
浏览文件 @ 43af22e4
from paddlenlp import Taskflow from paddlenlp import Taskflow
class NLP: class NLP:
def __init__(self, ie_model_path=None): def __init__(self, ie_model_path=None):
schema = ["时间", "出发地", "目的地", "费用"] schema = ["时间", "出发地", "目的地", "费用"]
if ie_model_path: if ie_model_path:
self.ie_model = Taskflow("information_extraction", self.ie_model = Taskflow(
schema=schema, task_path=ie_model_path) "information_extraction",
schema=schema,
task_path=ie_model_path)
else: else:
self.ie_model = Taskflow("information_extraction", self.ie_model = Taskflow("information_extraction", schema=schema)
schema=schema)
self.dialogue_model = Taskflow("dialogue") self.dialogue_model = Taskflow("dialogue")
def chat(self, text): def chat(self, text):
result = self.dialogue_model([text]) result = self.dialogue_model([text])
return result[0] return result[0]
def ie(self, text): def ie(self, text):
result = self.ie_model(text) result = self.ie_model(text)
return result return result
\ No newline at end of file
demos/speech_web/speech_server/src/SpeechBase/sql_helper.py
浏览文件 @ 43af22e4
import base64 import base64
import sqlite3
import os import os
import sqlite3
import numpy as np import numpy as np
from pkg_resources import resource_stream
def dict_factory(cursor, row): def dict_factory(cursor, row):
d = {} d = {}
for idx, col in enumerate(cursor.description): for idx, col in enumerate(cursor.description):
d[col[0]] = row[idx] d[col[0]] = row[idx]
return d return d
class DataBase(object): class DataBase(object):
def __init__(self, db_path:str): def __init__(self, db_path: str):
db_path = os.path.realpath(db_path) db_path = os.path.realpath(db_path)
if os.path.exists(db_path): if os.path.exists(db_path):
...@@ -21,12 +22,12 @@ class DataBase(object): ...@@ -21,12 +22,12 @@ class DataBase(object):
db_path_dir = os.path.dirname(db_path) db_path_dir = os.path.dirname(db_path)
os.makedirs(db_path_dir, exist_ok=True) os.makedirs(db_path_dir, exist_ok=True)
self.db_path = db_path self.db_path = db_path
self.conn = sqlite3.connect(self.db_path) self.conn = sqlite3.connect(self.db_path)
self.conn.row_factory = dict_factory self.conn.row_factory = dict_factory
self.cursor = self.conn.cursor() self.cursor = self.conn.cursor()
self.init_database() self.init_database()
def init_database(self): def init_database(self):
""" """
初始化数据库， 若表不存在则创建 初始化数据库， 若表不存在则创建
...@@ -41,20 +42,21 @@ class DataBase(object): ...@@ -41,20 +42,21 @@ class DataBase(object):
""" """
self.cursor.execute(sql) self.cursor.execute(sql)
self.conn.commit() self.conn.commit()
def execute_base(self, sql, data_dict): def execute_base(self, sql, data_dict):
self.cursor.execute(sql, data_dict) self.cursor.execute(sql, data_dict)
self.conn.commit() self.conn.commit()
def insert_one(self, username, vector_base64:str, wav_path): def insert_one(self, username, vector_base64: str, wav_path):
if not os.path.exists(wav_path): if not os.path.exists(wav_path):
return None, "wav not exists" return None, "wav not exists"
else: else:
sql = f""" sql = """
insert into insert into
vprtable (username, vector, wavpath) vprtable (username, vector, wavpath)
values (?, ?, ?) values (?, ?, ?)
""" """
try: try:
self.cursor.execute(sql, (username, vector_base64, wav_path)) self.cursor.execute(sql, (username, vector_base64, wav_path))
self.conn.commit() self.conn.commit()
...@@ -63,25 +65,27 @@ class DataBase(object): ...@@ -63,25 +65,27 @@ class DataBase(object):
except Exception as e: except Exception as e:
print(e) print(e)
return None, e return None, e
def select_all(self): def select_all(self):
sql = """ sql = """
SELECT * from vprtable SELECT * from vprtable
""" """
result = self.cursor.execute(sql).fetchall() result = self.cursor.execute(sql).fetchall()
return result return result
def select_by_id(self, vpr_id): def select_by_id(self, vpr_id):
sql = f""" sql = f"""
SELECT * from vprtable WHERE `id` = {vpr_id} SELECT * from vprtable WHERE `id` = {vpr_id}
""" """
result = self.cursor.execute(sql).fetchall() result = self.cursor.execute(sql).fetchall()
return result return result
def select_by_username(self, username): def select_by_username(self, username):
sql = f""" sql = f"""
SELECT * from vprtable WHERE `username` = '{username}' SELECT * from vprtable WHERE `username` = '{username}'
""" """
result = self.cursor.execute(sql).fetchall() result = self.cursor.execute(sql).fetchall()
return result return result
...@@ -89,28 +93,30 @@ class DataBase(object): ...@@ -89,28 +93,30 @@ class DataBase(object):
sql = f""" sql = f"""
DELETE from vprtable WHERE `username`='{username}' DELETE from vprtable WHERE `username`='{username}'
""" """
self.cursor.execute(sql) self.cursor.execute(sql)
self.conn.commit() self.conn.commit()
def drop_all(self): def drop_all(self):
sql = f""" sql = """
DELETE from vprtable DELETE from vprtable
""" """
self.cursor.execute(sql) self.cursor.execute(sql)
self.conn.commit() self.conn.commit()
def drop_table(self): def drop_table(self):
sql = f""" sql = """
DROP TABLE vprtable DROP TABLE vprtable
""" """
self.cursor.execute(sql) self.cursor.execute(sql)
self.conn.commit() self.conn.commit()
def encode_vector(self, vector:np.ndarray): def encode_vector(self, vector: np.ndarray):
return base64.b64encode(vector).decode('utf8') return base64.b64encode(vector).decode('utf8')
def decode_vector(self, vector_base64, dtype=np.float32): def decode_vector(self, vector_base64, dtype=np.float32):
b = base64.b64decode(vector_base64) b = base64.b64decode(vector_base64)
vc = np.frombuffer(b, dtype=dtype) vc = np.frombuffer(b, dtype=dtype)
return vc return vc
\ No newline at end of file
demos/speech_web/speech_server/src/SpeechBase/tts.py
浏览文件 @ 43af22e4
...@@ -5,18 +5,19 @@ ...@@ -5,18 +5,19 @@
# 2. 加载模型 # 2. 加载模型
# 3. 端到端推理 # 3. 端到端推理
# 4. 流式推理 # 4. 流式推理
import base64 import base64
import math
import logging import logging
import math
import numpy as np import numpy as np
from paddlespeech.server.utils.onnx_infer import get_sess
from paddlespeech.t2s.frontend.zh_frontend import Frontend from paddlespeech.server.engine.tts.online.onnx.tts_engine import TTSEngine
from paddlespeech.server.utils.util import denorm, get_chunks
from paddlespeech.server.utils.audio_process import float2pcm from paddlespeech.server.utils.audio_process import float2pcm
from paddlespeech.server.utils.config import get_config from paddlespeech.server.utils.config import get_config
from paddlespeech.server.utils.util import denorm
from paddlespeech.server.utils.util import get_chunks
from paddlespeech.t2s.frontend.zh_frontend import Frontend
from paddlespeech.server.engine.tts.online.onnx.tts_engine import TTSEngine
class TTS: class TTS:
def __init__(self, config_path): def __init__(self, config_path):
...@@ -26,12 +27,12 @@ class TTS: ...@@ -26,12 +27,12 @@ class TTS:
self.engine.init(self.config) self.engine.init(self.config)
self.executor = self.engine.executor self.executor = self.engine.executor
#self.engine.warm_up() #self.engine.warm_up()
# 前端初始化 # 前端初始化
self.frontend = Frontend( self.frontend = Frontend(
phone_vocab_path=self.engine.executor.phones_dict, phone_vocab_path=self.engine.executor.phones_dict,
tone_vocab_path=None) tone_vocab_path=None)
def depadding(self, data, chunk_num, chunk_id, block, pad, upsample): def depadding(self, data, chunk_num, chunk_id, block, pad, upsample):
""" """
Streaming inference removes the result of pad inference Streaming inference removes the result of pad inference
...@@ -48,39 +49,37 @@ class TTS: ...@@ -48,39 +49,37 @@ class TTS:
data = data[front_pad * upsample:(front_pad + block) * upsample] data = data[front_pad * upsample:(front_pad + block) * upsample]
return data return data
def offlineTTS(self, text): def offlineTTS(self, text):
get_tone_ids = False get_tone_ids = False
merge_sentences = False merge_sentences = False
input_ids = self.frontend.get_input_ids( input_ids = self.frontend.get_input_ids(
text, text, merge_sentences=merge_sentences, get_tone_ids=get_tone_ids)
merge_sentences=merge_sentences,
get_tone_ids=get_tone_ids)
phone_ids = input_ids["phone_ids"] phone_ids = input_ids["phone_ids"]
wav_list = [] wav_list = []
for i in range(len(phone_ids)): for i in range(len(phone_ids)):
orig_hs = self.engine.executor.am_encoder_infer_sess.run( orig_hs = self.engine.executor.am_encoder_infer_sess.run(
None, input_feed={'text': phone_ids[i].numpy()} None, input_feed={'text': phone_ids[i].numpy()})
)
hs = orig_hs[0] hs = orig_hs[0]
am_decoder_output = self.engine.executor.am_decoder_sess.run( am_decoder_output = self.engine.executor.am_decoder_sess.run(
None, input_feed={'xs': hs}) None, input_feed={'xs': hs})
am_postnet_output = self.engine.executor.am_postnet_sess.run( am_postnet_output = self.engine.executor.am_postnet_sess.run(
None, None,
input_feed={ input_feed={
'xs': np.transpose(am_decoder_output[0], (0, 2, 1)) 'xs': np.transpose(am_decoder_output[0], (0, 2, 1))
}) })
am_output_data = am_decoder_output + np.transpose( am_output_data = am_decoder_output + np.transpose(
am_postnet_output[0], (0, 2, 1)) am_postnet_output[0], (0, 2, 1))
normalized_mel = am_output_data[0][0] normalized_mel = am_output_data[0][0]
mel = denorm(normalized_mel, self.engine.executor.am_mu, self.engine.executor.am_std) mel = denorm(normalized_mel, self.engine.executor.am_mu,
self.engine.executor.am_std)
wav = self.engine.executor.voc_sess.run( wav = self.engine.executor.voc_sess.run(
output_names=None, input_feed={'logmel': mel})[0] output_names=None, input_feed={'logmel': mel})[0]
wav_list.append(wav) wav_list.append(wav)
wavs = np.concatenate(wav_list) wavs = np.concatenate(wav_list)
return wavs return wavs
def streamTTS(self, text): def streamTTS(self, text):
get_tone_ids = False get_tone_ids = False
...@@ -88,9 +87,7 @@ class TTS: ...@@ -88,9 +87,7 @@ class TTS:
# front # front
input_ids = self.frontend.get_input_ids( input_ids = self.frontend.get_input_ids(
text, text, merge_sentences=merge_sentences, get_tone_ids=get_tone_ids)
merge_sentences=merge_sentences,
get_tone_ids=get_tone_ids)
phone_ids = input_ids["phone_ids"] phone_ids = input_ids["phone_ids"]
for i in range(len(phone_ids)): for i in range(len(phone_ids)):
...@@ -105,14 +102,15 @@ class TTS: ...@@ -105,14 +102,15 @@ class TTS:
mel = mel[0] mel = mel[0]
# voc streaming # voc streaming
mel_chunks = get_chunks(mel, self.config.voc_block, self.config.voc_pad, "voc") mel_chunks = get_chunks(mel, self.config.voc_block,
self.config.voc_pad, "voc")
voc_chunk_num = len(mel_chunks) voc_chunk_num = len(mel_chunks)
for i, mel_chunk in enumerate(mel_chunks): for i, mel_chunk in enumerate(mel_chunks):
sub_wav = self.executor.voc_sess.run( sub_wav = self.executor.voc_sess.run(
output_names=None, input_feed={'logmel': mel_chunk}) output_names=None, input_feed={'logmel': mel_chunk})
sub_wav = self.depadding(sub_wav[0], voc_chunk_num, i, sub_wav = self.depadding(
self.config.voc_block, self.config.voc_pad, sub_wav[0], voc_chunk_num, i, self.config.voc_block,
self.config.voc_upsample) self.config.voc_pad, self.config.voc_upsample)
yield self.after_process(sub_wav) yield self.after_process(sub_wav)
...@@ -130,7 +128,8 @@ class TTS: ...@@ -130,7 +128,8 @@ class TTS:
end = min(self.config.voc_block + self.config.voc_pad, mel_len) end = min(self.config.voc_block + self.config.voc_pad, mel_len)
# streaming am # streaming am
hss = get_chunks(orig_hs, self.config.am_block, self.config.am_pad, "am") hss = get_chunks(orig_hs, self.config.am_block,
self.config.am_pad, "am")
am_chunk_num = len(hss) am_chunk_num = len(hss)
for i, hs in enumerate(hss): for i, hs in enumerate(hss):
am_decoder_output = self.executor.am_decoder_sess.run( am_decoder_output = self.executor.am_decoder_sess.run(
...@@ -147,7 +146,8 @@ class TTS: ...@@ -147,7 +146,8 @@ class TTS:
sub_mel = denorm(normalized_mel, self.executor.am_mu, sub_mel = denorm(normalized_mel, self.executor.am_mu,
self.executor.am_std) self.executor.am_std)
sub_mel = self.depadding(sub_mel, am_chunk_num, i, sub_mel = self.depadding(sub_mel, am_chunk_num, i,
self.config.am_block, self.config.am_pad, 1) self.config.am_block,
self.config.am_pad, 1)
if i == 0: if i == 0:
mel_streaming = sub_mel mel_streaming = sub_mel
...@@ -165,23 +165,22 @@ class TTS: ...@@ -165,23 +165,22 @@ class TTS:
output_names=None, input_feed={'logmel': voc_chunk}) output_names=None, input_feed={'logmel': voc_chunk})
sub_wav = self.depadding( sub_wav = self.depadding(
sub_wav[0], voc_chunk_num, voc_chunk_id, sub_wav[0], voc_chunk_num, voc_chunk_id,
self.config.voc_block, self.config.voc_pad, self.config.voc_upsample) self.config.voc_block, self.config.voc_pad,
self.config.voc_upsample)
yield self.after_process(sub_wav) yield self.after_process(sub_wav)
voc_chunk_id += 1 voc_chunk_id += 1
start = max( start = max(0, voc_chunk_id * self.config.voc_block -
0, voc_chunk_id * self.config.voc_block - self.config.voc_pad) self.config.voc_pad)
end = min( end = min((voc_chunk_id + 1) * self.config.voc_block +
(voc_chunk_id + 1) * self.config.voc_block + self.config.voc_pad, self.config.voc_pad, mel_len)
mel_len)
else: else:
logging.error( logging.error(
"Only support fastspeech2_csmsc or fastspeech2_cnndecoder_csmsc on streaming tts." "Only support fastspeech2_csmsc or fastspeech2_cnndecoder_csmsc on streaming tts."
) )
def streamTTSBytes(self, text): def streamTTSBytes(self, text):
for wav in self.engine.executor.infer( for wav in self.engine.executor.infer(
text=text, text=text,
...@@ -191,19 +190,14 @@ class TTS: ...@@ -191,19 +190,14 @@ class TTS:
wav = float2pcm(wav) # float32 to int16 wav = float2pcm(wav) # float32 to int16
wav_bytes = wav.tobytes() # to bytes wav_bytes = wav.tobytes() # to bytes
yield wav_bytes yield wav_bytes
def after_process(self, wav): def after_process(self, wav):
# for tvm # for tvm
wav = float2pcm(wav) # float32 to int16 wav = float2pcm(wav) # float32 to int16
wav_bytes = wav.tobytes() # to bytes wav_bytes = wav.tobytes() # to bytes
wav_base64 = base64.b64encode(wav_bytes).decode('utf8') # to base64 wav_base64 = base64.b64encode(wav_bytes).decode('utf8') # to base64
return wav_base64 return wav_base64
def streamTTS_TVM(self, text): def streamTTS_TVM(self, text):
# 用 TVM 优化 # 用 TVM 优化
pass pass
\ No newline at end of file
demos/speech_web/speech_server/src/SpeechBase/vpr.py
浏览文件 @ 43af22e4
# vpr Demo 没有使用 mysql 与 muilvs, 仅用于docker演示 # vpr Demo 没有使用 mysql 与 muilvs, 仅用于docker演示
import logging import logging
import faiss import faiss
from matplotlib import use
import numpy as np import numpy as np
from .sql_helper import DataBase from .sql_helper import DataBase
from .vpr_encode import get_audio_embedding from .vpr_encode import get_audio_embedding
class VPR: class VPR:
def __init__(self, db_path, dim, top_k) -> None: def __init__(self, db_path, dim, top_k) -> None:
# 初始化 # 初始化
...@@ -14,15 +16,15 @@ class VPR: ...@@ -14,15 +16,15 @@ class VPR:
self.top_k = top_k self.top_k = top_k
self.dtype = np.float32 self.dtype = np.float32
self.vpr_idx = 0 self.vpr_idx = 0
# db 初始化 # db 初始化
self.db = DataBase(db_path) self.db = DataBase(db_path)
# faiss 初始化 # faiss 初始化
index_ip = faiss.IndexFlatIP(dim) index_ip = faiss.IndexFlatIP(dim)
self.index_ip = faiss.IndexIDMap(index_ip) self.index_ip = faiss.IndexIDMap(index_ip)
self.init() self.init()
def init(self): def init(self):
# demo 初始化，把 mysql中的向量注册到 faiss 中 # demo 初始化，把 mysql中的向量注册到 faiss 中
sql_dbs = self.db.select_all() sql_dbs = self.db.select_all()
...@@ -34,12 +36,13 @@ class VPR: ...@@ -34,12 +36,13 @@ class VPR:
if len(vc.shape) == 1: if len(vc.shape) == 1:
vc = np.expand_dims(vc, axis=0) vc = np.expand_dims(vc, axis=0)
# 构建数据库 # 构建数据库
self.index_ip.add_with_ids(vc, np.array((idx,)).astype('int64')) self.index_ip.add_with_ids(vc, np.array(
(idx, )).astype('int64'))
logging.info("faiss 构建完毕") logging.info("faiss 构建完毕")
def faiss_enroll(self, idx, vc): def faiss_enroll(self, idx, vc):
self.index_ip.add_with_ids(vc, np.array((idx,)).astype('int64')) self.index_ip.add_with_ids(vc, np.array((idx, )).astype('int64'))
def vpr_enroll(self, username, wav_path): def vpr_enroll(self, username, wav_path):
# 注册声纹 # 注册声纹
emb = get_audio_embedding(wav_path) emb = get_audio_embedding(wav_path)
...@@ -53,21 +56,22 @@ class VPR: ...@@ -53,21 +56,22 @@ class VPR:
else: else:
last_idx, mess = None last_idx, mess = None
return last_idx return last_idx
def vpr_recog(self, wav_path): def vpr_recog(self, wav_path):
# 识别声纹 # 识别声纹
emb_search = get_audio_embedding(wav_path) emb_search = get_audio_embedding(wav_path)
if emb_search is not None: if emb_search is not None:
emb_search = np.expand_dims(emb_search, axis=0) emb_search = np.expand_dims(emb_search, axis=0)
D, I = self.index_ip.search(emb_search, self.top_k) D, I = self.index_ip.search(emb_search, self.top_k)
D = D.tolist()[0] D = D.tolist()[0]
I = I.tolist()[0] I = I.tolist()[0]
return [(round(D[i] * 100, 2 ), I[i]) for i in range(len(D)) if I[i] != -1] return [(round(D[i] * 100, 2), I[i]) for i in range(len(D))
if I[i] != -1]
else: else:
logging.error("识别失败") logging.error("识别失败")
return None return None
def do_search_vpr(self, wav_path): def do_search_vpr(self, wav_path):
spk_ids, paths, scores = [], [], [] spk_ids, paths, scores = [], [], []
recog_result = self.vpr_recog(wav_path) recog_result = self.vpr_recog(wav_path)
...@@ -78,41 +82,39 @@ class VPR: ...@@ -78,41 +82,39 @@ class VPR:
scores.append(score) scores.append(score)
paths.append("") paths.append("")
return spk_ids, paths, scores return spk_ids, paths, scores
def vpr_del(self, username): def vpr_del(self, username):
# 根据用户username, 删除声纹 # 根据用户username, 删除声纹
# 查用户ID，删除对应向量 # 查用户ID，删除对应向量
res = self.db.select_by_username(username) res = self.db.select_by_username(username)
for r in res: for r in res:
idx = r['id'] idx = r['id']
self.index_ip.remove_ids(np.array((idx,)).astype('int64')) self.index_ip.remove_ids(np.array((idx, )).astype('int64'))
self.db.drop_by_username(username) self.db.drop_by_username(username)
def vpr_list(self): def vpr_list(self):
# 获取数据列表 # 获取数据列表
return self.db.select_all() return self.db.select_all()
def do_list(self): def do_list(self):
spk_ids, vpr_ids = [], [] spk_ids, vpr_ids = [], []
for res in self.db.select_all(): for res in self.db.select_all():
spk_ids.append(res['username']) spk_ids.append(res['username'])
vpr_ids.append(res['id']) vpr_ids.append(res['id'])
return spk_ids, vpr_ids return spk_ids, vpr_ids
def do_get_wav(self, vpr_idx): def do_get_wav(self, vpr_idx):
res = self.db.select_by_id(vpr_idx) res = self.db.select_by_id(vpr_idx)
return res[0]['wavpath'] return res[0]['wavpath']
def vpr_data(self, idx): def vpr_data(self, idx):
# 获取对应ID的数据 # 获取对应ID的数据
res = self.db.select_by_id(idx) res = self.db.select_by_id(idx)
return res return res
def vpr_droptable(self): def vpr_droptable(self):
# 删除表 # 删除表
self.db.drop_table() self.db.drop_table()
# 清空 faiss # 清空 faiss
self.index_ip.reset() self.index_ip.reset()
demos/speech_web/speech_server/src/SpeechBase/vpr_encode.py
浏览文件 @ 43af22e4
from paddlespeech.cli.vector import VectorExecutor
import numpy as np
import logging import logging
import numpy as np
from paddlespeech.cli.vector import VectorExecutor
vector_executor = VectorExecutor() vector_executor = VectorExecutor()
def get_audio_embedding(path): def get_audio_embedding(path):
""" """
Use vpr_inference to generate embedding of audio Use vpr_inference to generate embedding of audio
...@@ -16,5 +19,3 @@ def get_audio_embedding(path): ...@@ -16,5 +19,3 @@ def get_audio_embedding(path):
except Exception as e: except Exception as e:
logging.error(f"Error with embedding:{e}") logging.error(f"Error with embedding:{e}")
return None return None
\ No newline at end of file
demos/speech_web/speech_server/src/WebsocketManeger.py
浏览文件 @ 43af22e4
...@@ -2,6 +2,7 @@ from typing import List ...@@ -2,6 +2,7 @@ from typing import List
from fastapi import WebSocket from fastapi import WebSocket
class ConnectionManager: class ConnectionManager:
def __init__(self): def __init__(self):
# 存放激活的ws连接对象 # 存放激活的ws连接对象
...@@ -28,4 +29,4 @@ class ConnectionManager: ...@@ -28,4 +29,4 @@ class ConnectionManager:
await connection.send_text(message) await connection.send_text(message)
manager = ConnectionManager() manager = ConnectionManager()
\ No newline at end of file
demos/speech_web/speech_server/src/robot.py
浏览文件 @ 43af22e4
from paddlespeech.cli.asr.infer import ASRExecutor
import soundfile as sf
import os import os
import librosa
import soundfile as sf
from src.SpeechBase.asr import ASR from src.SpeechBase.asr import ASR
from src.SpeechBase.tts import TTS
from src.SpeechBase.nlp import NLP from src.SpeechBase.nlp import NLP
from src.SpeechBase.tts import TTS
from paddlespeech.cli.asr.infer import ASRExecutor
class Robot: class Robot:
def __init__(self, asr_config, tts_config,asr_init_path, def __init__(self,
asr_config,
tts_config,
asr_init_path,
ie_model_path=None) -> None: ie_model_path=None) -> None:
self.nlp = NLP(ie_model_path=ie_model_path) self.nlp = NLP(ie_model_path=ie_model_path)
self.asr = ASR(config_path=asr_config) self.asr = ASR(config_path=asr_config)
self.tts = TTS(config_path=tts_config) self.tts = TTS(config_path=tts_config)
self.tts_sample_rate = 24000 self.tts_sample_rate = 24000
self.asr_sample_rate = 16000 self.asr_sample_rate = 16000
# 流式识别效果不如端到端的模型，这里流式模型与端到端模型分开 # 流式识别效果不如端到端的模型，这里流式模型与端到端模型分开
self.asr_model = ASRExecutor() self.asr_model = ASRExecutor()
self.asr_name = "conformer_wenetspeech" self.asr_name = "conformer_wenetspeech"
self.warm_up_asrmodel(asr_init_path) self.warm_up_asrmodel(asr_init_path)
def warm_up_asrmodel(self, asr_init_path): def warm_up_asrmodel(self, asr_init_path):
if not os.path.exists(asr_init_path): if not os.path.exists(asr_init_path):
path_dir = os.path.dirname(asr_init_path) path_dir = os.path.dirname(asr_init_path)
if not os.path.exists(path_dir): if not os.path.exists(path_dir):
os.makedirs(path_dir, exist_ok=True) os.makedirs(path_dir, exist_ok=True)
# TTS生成，采样率24000 # TTS生成，采样率24000
text = "生成初始音频" text = "生成初始音频"
self.text2speech(text, asr_init_path) self.text2speech(text, asr_init_path)
# asr model初始化 # asr model初始化
self.asr_model(asr_init_path, model=self.asr_name,lang='zh', self.asr_model(
sample_rate=16000, force_yes=True) asr_init_path,
model=self.asr_name,
lang='zh',
sample_rate=16000,
force_yes=True)
def speech2text(self, audio_file): def speech2text(self, audio_file):
self.asr_model.preprocess(self.asr_name, audio_file) self.asr_model.preprocess(self.asr_name, audio_file)
self.asr_model.infer(self.asr_name) self.asr_model.infer(self.asr_name)
res = self.asr_model.postprocess() res = self.asr_model.postprocess()
return res return res
def text2speech(self, text, outpath): def text2speech(self, text, outpath):
wav = self.tts.offlineTTS(text) wav = self.tts.offlineTTS(text)
sf.write( sf.write(outpath, wav, samplerate=self.tts_sample_rate)
outpath, wav, samplerate=self.tts_sample_rate)
res = wav res = wav
return res return res
def text2speechStream(self, text): def text2speechStream(self, text):
for sub_wav_base64 in self.tts.streamTTS(text=text): for sub_wav_base64 in self.tts.streamTTS(text=text):
yield sub_wav_base64 yield sub_wav_base64
def text2speechStreamBytes(self, text): def text2speechStreamBytes(self, text):
for wav_bytes in self.tts.streamTTSBytes(text=text): for wav_bytes in self.tts.streamTTSBytes(text=text):
yield wav_bytes yield wav_bytes
...@@ -66,5 +70,3 @@ class Robot: ...@@ -66,5 +70,3 @@ class Robot:
def ie(self, text): def ie(self, text):
result = self.nlp.ie(text) result = self.nlp.ie(text)
return result return result
\ No newline at end of file
demos/speech_web/speech_server/src/util.py
浏览文件 @ 43af22e4
import random import random
def randName(n=5): def randName(n=5):
return "".join(random.sample('zyxwvutsrqponmlkjihgfedcba',n)) return "".join(random.sample('zyxwvutsrqponmlkjihgfedcba', n))
def SuccessRequest(result=None, message="ok"): def SuccessRequest(result=None, message="ok"):
return { return {"code": 0, "result": result, "message": message}
"code": 0,
"result":result,
"message": message
}
def ErrorRequest(result=None, message="error"): def ErrorRequest(result=None, message="error"):
return { return {"code": -1, "result": result, "message": message}
"code": -1,
"result":result,
"message": message
}
\ No newline at end of file
demos/streaming_asr_server/local/rtf_from_log.py
浏览文件 @ 43af22e4
...@@ -34,7 +34,7 @@ if __name__ == '__main__': ...@@ -34,7 +34,7 @@ if __name__ == '__main__':
n = 0 n = 0
for m in rtfs: for m in rtfs:
# not accurate, may have duplicate log # not accurate, may have duplicate log
n += 1 n += 1
T += m['T'] T += m['T']
P += m['P'] P += m['P']
......
docs/requirements.txt
浏览文件 @ 43af22e4
myst-parser braceexpandcolorlog
numpydoc
recommonmark>=0.5.0
sphinx
sphinx-autobuild
sphinx-markdown-tables
sphinx_rtd_theme
paddlepaddle>=2.2.2
editdistance editdistance
fastapi
g2p_en g2p_en
g2pM g2pM
h5py h5py
...@@ -14,40 +8,45 @@ inflect ...@@ -14,40 +8,45 @@ inflect
jieba jieba
jsonlines jsonlines
kaldiio kaldiio
keyboard
librosa==0.8.1 librosa==0.8.1
loguru loguru
matplotlib matplotlib
myst-parser
nara_wpe nara_wpe
numpydoc
onnxruntime==1.10.0 onnxruntime==1.10.0
opencc opencc
pandas
paddlenlp paddlenlp
paddlepaddle>=2.2.2
paddlespeech_feat paddlespeech_feat
pandas
pathos == 0.2.8
pattern_singleton
Pillow>=9.0.0 Pillow>=9.0.0
praatio==5.0.0 praatio==5.0.0
prettytable
pypinyin pypinyin
pypinyin-dict pypinyin-dict
python-dateutil python-dateutil
pyworld==0.2.12 pyworld==0.2.12
recommonmark>=0.5.0
resampy==0.2.2 resampy==0.2.2
sacrebleu sacrebleu
scipy scipy
sentencepiece~=0.1.96 sentencepiece~=0.1.96
soundfile~=0.10 soundfile~=0.10
sphinx
sphinx-autobuild
sphinx-markdown-tables
sphinx_rtd_theme
textgrid textgrid
timer timer
tqdm tqdm
typeguard typeguard
uvicorn
visualdl visualdl
webrtcvad webrtcvad
websockets
yacs~=0.1.8 yacs~=0.1.8
prettytable
zhon zhon
colorlog
pathos == 0.2.8
fastapi
websockets
keyboard
uvicorn
pattern_singleton
braceexpand
\ No newline at end of file
docs/source/conf.py
浏览文件 @ 43af22e4
...@@ -20,10 +20,11 @@ ...@@ -20,10 +20,11 @@
# If extensions (or modules to document with autodoc) are in another directory, # If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the # add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here. # documentation root, use os.path.abspath to make it absolute, like shown here.
import os
import sys
import recommonmark.parser import recommonmark.parser
import sphinx_rtd_theme import sphinx_rtd_theme
import sys
import os
sys.path.insert(0, os.path.abspath('../..')) sys.path.insert(0, os.path.abspath('../..'))
autodoc_mock_imports = ["soundfile", "librosa"] autodoc_mock_imports = ["soundfile", "librosa"]
......
examples/aishell3/tts3/run.sh
浏览文件 @ 43af22e4
...@@ -44,8 +44,8 @@ fi ...@@ -44,8 +44,8 @@ fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# install paddle2onnx # install paddle2onnx
version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
if [[ -z "$version" || ${version} != '0.9.8' ]]; then if [[ -z "$version" || ${version} != '1.0.0' ]]; then
pip install paddle2onnx==0.9.8 pip install paddle2onnx==1.0.0
fi fi
./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_aishell3 ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_aishell3
# considering the balance between speed and quality, we recommend that you use hifigan as vocoder # considering the balance between speed and quality, we recommend that you use hifigan as vocoder
......
examples/aishell3/vits-vc/README.md 0 → 100644
浏览文件 @ 43af22e4
# VITS with AISHELL-3
This example contains code used to train a [VITS](https://arxiv.org/abs/2106.06103) model with [AISHELL-3](http://www.aishelltech.com/aishell_3). The trained model can be used in Voice Cloning Task, We refer to the model structure of [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://arxiv.org/pdf/1806.04558.pdf). The general steps are as follows:
1. Speaker Encoder: We use Speaker Verification to train a speaker encoder. Datasets used in this task are different from those used in `VITS` because the transcriptions are not needed, we use more datasets, refer to [ge2e](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/ge2e).
2. Synthesizer and Vocoder: We use the trained speaker encoder to generate speaker embedding for each sentence in AISHELL-3. This embedding is an extra input of `VITS` which will be concated with encoder outputs. The vocoder is part of `VITS` due to its special structure.
## Dataset
### Download and Extract
Download AISHELL-3 from it's [Official Website](http://www.aishelltech.com/aishell_3) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/data_aishell3`.
### Get MFA Result and Extract
We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get phonemes for VITS, the durations of MFA are not needed here.
You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) (use MFA1.x now) of our repo.
## Pretrained GE2E Model
We use pretrained GE2E model to generate speaker embedding for each sentence.
Download pretrained GE2E model from here [ge2e_ckpt_0.3.zip](https://bj.bcebos.com/paddlespeech/Parakeet/released_models/ge2e/ge2e_ckpt_0.3.zip), and `unzip` it.
## Get Started
Assume the path to the dataset is `~/datasets/data_aishell3`.
Assume the path to the MFA result of AISHELL-3 is `./aishell3_alignment_tone`.
Assume the path to the pretrained ge2e model is `./ge2e_ckpt_0.3`.
Run the command below to
1. **source path**.
2. preprocess the dataset.
3. train the model.
4. synthesize waveform from `metadata.jsonl`.
5. start a voice cloning inference.
```bash
./run.sh
```
You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, running the following command will only preprocess the dataset.
```bash
./run.sh --stage 0 --stop-stage 0
```
### Data Preprocessing
```bash
CUDA_VISIBLE_DEVICES=${gpus} ./local/preprocess.sh ${conf_path} ${ge2e_ckpt_path}
```
When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
```text
dump
├── dev
│   ├── norm
│   └── raw
├── embed
│ ├── SSB0005
│ ├── SSB0009
│ ├── ...
│ └── ...
├── phone_id_map.txt
├── speaker_id_map.txt
├── test
│   ├── norm
│   └── raw
└── train
├── feats_stats.npy
├── norm
└── raw
```
The `embed` contains the generated speaker embedding for each sentence in AISHELL-3, which has the same file structure with wav files and the format is `.npy`.
The computing time of utterance embedding can be x hours.
The dataset is split into 3 parts, namely `train`, `dev`, and` test`, each of which contains a `norm` and `raw` subfolder. The raw folder contains wave and linear spectrogram of each utterance, while the norm folder contains normalized ones. The statistics used to normalize features are computed from the training set, which is located in `dump/train/feats_stats.npy`.
Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains phones, text_lengths, feats, feats_lengths, the path of linear spectrogram features, the path of raw waves, speaker, and the id of each utterance.
The preprocessing step is very similar to that one of [vits](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/vits), but there is one more `ge2e/inference` step here.
### Model Training
```bash
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
```
The training step is very similar to that one of [vits](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/vits), but we should set `--voice-cloning=True` when calling `${BIN_DIR}/train.py`.
### Synthesizing
`./local/synthesize.sh` calls `${BIN_DIR}/synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
```bash
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
```
```text
usage: synthesize.py [-h] [--config CONFIG] [--ckpt CKPT]
[--phones_dict PHONES_DICT] [--speaker_dict SPEAKER_DICT]
[--voice-cloning VOICE_CLONING] [--ngpu NGPU]
[--test_metadata TEST_METADATA] [--output_dir OUTPUT_DIR]
Synthesize with VITS
optional arguments:
-h, --help show this help message and exit
--config CONFIG Config of VITS.
--ckpt CKPT Checkpoint file of VITS.
--phones_dict PHONES_DICT
phone vocabulary file.
--speaker_dict SPEAKER_DICT
speaker id map file.
--voice-cloning VOICE_CLONING
whether training voice cloning model.
--ngpu NGPU if ngpu == 0, use cpu.
--test_metadata TEST_METADATA
test metadata.
--output_dir OUTPUT_DIR
output dir.
```
The synthesizing step is very similar to that one of [vits](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/vits), but we should set `--voice-cloning=True` when calling `${BIN_DIR}/../synthesize.py`.
### Voice Cloning
Assume there are some reference audios in `./ref_audio`
```text
ref_audio
├── 001238.wav
├── LJ015-0254.wav
└── audio_self_test.mp3
```
`./local/voice_cloning.sh` calls `${BIN_DIR}/voice_cloning.py`
```bash
CUDA_VISIBLE_DEVICES=${gpus} ./local/voice_cloning.sh ${conf_path} ${train_output_path} ${ckpt_name} ${ge2e_params_path} ${add_blank} ${ref_audio_dir}
```
If you want to convert a speaker audio file to refered speaker, run:
```bash
CUDA_VISIBLE_DEVICES=${gpus} ./local/voice_cloning.sh ${conf_path} ${train_output_path} ${ckpt_name} ${ge2e_params_path} ${add_blank} ${ref_audio_dir} ${src_audio_path}
```
<!-- TODO display these after we trained the model -->
<!--
## Pretrained Model
The pretrained model can be downloaded here:
- [vits_vc_aishell3_ckpt_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/vits/vits_vc_aishell3_ckpt_1.1.0.zip) (add_blank=true)
VITS checkpoint contains files listed below.
(There is no need for `speaker_id_map.txt` here )
```text
vits_vc_aishell3_ckpt_1.1.0
├── default.yaml # default config used to train vitx
├── phone_id_map.txt # phone vocabulary file when training vits
└── snapshot_iter_333000.pdz # model parameters and optimizer states
```
ps: This ckpt is not good enough, a better result is training
-->
examples/aishell3/vits-vc/conf/default.yaml 0 → 100644
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# This configuration tested on 4 GPUs (V100) with 32GB GPU
# memory. It takes around 2 weeks to finish the training
# but 100k iters model should generate reasonable results.
###########################################################
# FEATURE EXTRACTION SETTING #
###########################################################
fs: 22050 # sr
n_fft: 1024 # FFT size (samples).
n_shift: 256 # Hop size (samples). 12.5ms
win_length: null # Window length (samples). 50ms
# If set to null, it will be the same as fft_size.
window: "hann" # Window function.
##########################################################
# TTS MODEL SETTING #
##########################################################
model:
# generator related
generator_type: vits_generator
generator_params:
hidden_channels: 192
spk_embed_dim: 256
global_channels: 256
segment_size: 32
text_encoder_attention_heads: 2
text_encoder_ffn_expand: 4
text_encoder_blocks: 6
text_encoder_positionwise_layer_type: "conv1d"
text_encoder_positionwise_conv_kernel_size: 3
text_encoder_positional_encoding_layer_type: "rel_pos"
text_encoder_self_attention_layer_type: "rel_selfattn"
text_encoder_activation_type: "swish"
text_encoder_normalize_before: True
text_encoder_dropout_rate: 0.1
text_encoder_positional_dropout_rate: 0.0
text_encoder_attention_dropout_rate: 0.1
use_macaron_style_in_text_encoder: True
use_conformer_conv_in_text_encoder: False
text_encoder_conformer_kernel_size: -1
decoder_kernel_size: 7
decoder_channels: 512
decoder_upsample_scales: [8, 8, 2, 2]
decoder_upsample_kernel_sizes: [16, 16, 4, 4]
decoder_resblock_kernel_sizes: [3, 7, 11]
decoder_resblock_dilations: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
use_weight_norm_in_decoder: True
posterior_encoder_kernel_size: 5
posterior_encoder_layers: 16
posterior_encoder_stacks: 1
posterior_encoder_base_dilation: 1
posterior_encoder_dropout_rate: 0.0
use_weight_norm_in_posterior_encoder: True
flow_flows: 4
flow_kernel_size: 5
flow_base_dilation: 1
flow_layers: 4
flow_dropout_rate: 0.0
use_weight_norm_in_flow: True
use_only_mean_in_flow: True
stochastic_duration_predictor_kernel_size: 3
stochastic_duration_predictor_dropout_rate: 0.5
stochastic_duration_predictor_flows: 4
stochastic_duration_predictor_dds_conv_layers: 3
# discriminator related
discriminator_type: hifigan_multi_scale_multi_period_discriminator
discriminator_params:
scales: 1
scale_downsample_pooling: "AvgPool1D"
scale_downsample_pooling_params:
kernel_size: 4
stride: 2
padding: 2
scale_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes: [15, 41, 5, 3]
channels: 128
max_downsample_channels: 1024
max_groups: 16
bias: True
downsample_scales: [2, 2, 4, 4, 1]
nonlinear_activation: "leakyrelu"
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: True
use_spectral_norm: False
follow_official_norm: False
periods: [2, 3, 5, 7, 11]
period_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes: [5, 3]
channels: 32
downsample_scales: [3, 3, 3, 3, 1]
max_downsample_channels: 1024
bias: True
nonlinear_activation: "leakyrelu"
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: True
use_spectral_norm: False
# others
sampling_rate: 22050 # needed in the inference for saving wav
cache_generator_outputs: True # whether to cache generator outputs in the training
###########################################################
# LOSS SETTING #
###########################################################
# loss function related
generator_adv_loss_params:
average_by_discriminators: False # whether to average loss value by #discriminators
loss_type: mse # loss type, "mse" or "hinge"
discriminator_adv_loss_params:
average_by_discriminators: False # whether to average loss value by #discriminators
loss_type: mse # loss type, "mse" or "hinge"
feat_match_loss_params:
average_by_discriminators: False # whether to average loss value by #discriminators
average_by_layers: False # whether to average loss value by #layers of each discriminator
include_final_outputs: True # whether to include final outputs for loss calculation
mel_loss_params:
fs: 22050 # must be the same as the training data
fft_size: 1024 # fft points
hop_size: 256 # hop size
win_length: null # window length
window: hann # window type
num_mels: 80 # number of Mel basis
fmin: 0 # minimum frequency for Mel basis
fmax: null # maximum frequency for Mel basis
log_base: null # null represent natural log
###########################################################
# ADVERSARIAL LOSS SETTING #
###########################################################
lambda_adv: 1.0 # loss scaling coefficient for adversarial loss
lambda_mel: 45.0 # loss scaling coefficient for Mel loss
lambda_feat_match: 2.0 # loss scaling coefficient for feat match loss
lambda_dur: 1.0 # loss scaling coefficient for duration loss
lambda_kl: 1.0 # loss scaling coefficient for KL divergence loss
# others
sampling_rate: 22050 # needed in the inference for saving wav
cache_generator_outputs: True # whether to cache generator outputs in the training
###########################################################
# DATA LOADER SETTING #
###########################################################
batch_size: 50 # Batch size.
num_workers: 4 # Number of workers in DataLoader.
##########################################################
# OPTIMIZER & SCHEDULER SETTING #
##########################################################
# optimizer setting for generator
generator_optimizer_params:
beta1: 0.8
beta2: 0.99
epsilon: 1.0e-9
weight_decay: 0.0
generator_scheduler: exponential_decay
generator_scheduler_params:
learning_rate: 2.0e-4
gamma: 0.999875
# optimizer setting for discriminator
discriminator_optimizer_params:
beta1: 0.8
beta2: 0.99
epsilon: 1.0e-9
weight_decay: 0.0
discriminator_scheduler: exponential_decay
discriminator_scheduler_params:
learning_rate: 2.0e-4
gamma: 0.999875
generator_first: False # whether to start updating generator first
##########################################################
# OTHER TRAINING SETTING #
##########################################################
num_snapshots: 10 # max number of snapshots to keep while training
train_max_steps: 350000 # Number of training steps. == total_iters / ngpus, total_iters = 1000000
save_interval_steps: 1000 # Interval steps to save checkpoint.
eval_interval_steps: 250 # Interval steps to evaluate the network.
seed: 777 # random seed number
examples/aishell3/vits-vc/local/preprocess.sh 0 → 100755
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#!/bin/bash
stage=0
stop_stage=100
config_path=$1
add_blank=$2
ge2e_ckpt_path=$3
# gen speaker embedding
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
python3 ${MAIN_ROOT}/paddlespeech/vector/exps/ge2e/inference.py \
--input=~/datasets/data_aishell3/train/wav/ \
--output=dump/embed \
--checkpoint_path=${ge2e_ckpt_path}
fi
# copy from tts3/preprocess
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# get durations from MFA's result
echo "Generate durations.txt from MFA results ..."
python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
--inputdir=./aishell3_alignment_tone \
--output durations.txt \
--config=${config_path}
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# extract features
echo "Extract features ..."
python3 ${BIN_DIR}/preprocess.py \
--dataset=aishell3 \
--rootdir=~/datasets/data_aishell3/ \
--dumpdir=dump \
--dur-file=durations.txt \
--config=${config_path} \
--num-cpu=20 \
--cut-sil=True \
--spk_emb_dir=dump/embed
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# get features' stats(mean and std)
echo "Get features' stats ..."
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
--metadata=dump/train/raw/metadata.jsonl \
--field-name="feats"
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# normalize and covert phone/speaker to id, dev and test should use train's stats
echo "Normalize ..."
python3 ${BIN_DIR}/normalize.py \
--metadata=dump/train/raw/metadata.jsonl \
--dumpdir=dump/train/norm \
--feats-stats=dump/train/feats_stats.npy \
--phones-dict=dump/phone_id_map.txt \
--speaker-dict=dump/speaker_id_map.txt \
--add-blank=${add_blank} \
--skip-wav-copy
python3 ${BIN_DIR}/normalize.py \
--metadata=dump/dev/raw/metadata.jsonl \
--dumpdir=dump/dev/norm \
--feats-stats=dump/train/feats_stats.npy \
--phones-dict=dump/phone_id_map.txt \
--speaker-dict=dump/speaker_id_map.txt \
--add-blank=${add_blank} \
--skip-wav-copy
python3 ${BIN_DIR}/normalize.py \
--metadata=dump/test/raw/metadata.jsonl \
--dumpdir=dump/test/norm \
--feats-stats=dump/train/feats_stats.npy \
--phones-dict=dump/phone_id_map.txt \
--speaker-dict=dump/speaker_id_map.txt \
--add-blank=${add_blank} \
--skip-wav-copy
fi
examples/aishell3/vits-vc/local/synthesize.sh 0 → 100755
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#!/bin/bash
config_path=$1
train_output_path=$2
ckpt_name=$3
stage=0
stop_stage=0
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/synthesize.py \
--config=${config_path} \
--ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--phones_dict=dump/phone_id_map.txt \
--test_metadata=dump/test/norm/metadata.jsonl \
--output_dir=${train_output_path}/test \
--voice-cloning=True
fi
examples/aishell3/vits-vc/local/train.sh 0 → 100755
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#!/bin/bash
config_path=$1
train_output_path=$2
# install monotonic_align
cd ${MAIN_ROOT}/paddlespeech/t2s/models/vits/monotonic_align
python3 setup.py build_ext --inplace
cd -
python3 ${BIN_DIR}/train.py \
--train-metadata=dump/train/norm/metadata.jsonl \
--dev-metadata=dump/dev/norm/metadata.jsonl \
--config=${config_path} \
--output-dir=${train_output_path} \
--ngpu=4 \
--phones-dict=dump/phone_id_map.txt \
--voice-cloning=True
examples/aishell3/vits-vc/local/voice_cloning.sh 0 → 100755
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#!/bin/bash
config_path=$1
train_output_path=$2
ckpt_name=$3
ge2e_params_path=$4
add_blank=$5
ref_audio_dir=$6
src_audio_path=$7
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/voice_cloning.py \
--config=${config_path} \
--ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--ge2e_params_path=${ge2e_params_path} \
--phones_dict=dump/phone_id_map.txt \
--text="凯莫瑞安联合体的经济崩溃迫在眉睫。" \
--audio-path=${src_audio_path} \
--input-dir=${ref_audio_dir} \
--output-dir=${train_output_path}/vc_syn \
--add-blank=${add_blank}
examples/aishell3/vits-vc/path.sh 0 → 100755
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#!/bin/bash
export MAIN_ROOT=`realpath ${PWD}/../../../`
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
export LC_ALL=C
export PYTHONDONTWRITEBYTECODE=1
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
export PYTHONIOENCODING=UTF-8
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
MODEL=vits
export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}
\ No newline at end of file
examples/aishell3/vits-vc/run.sh 0 → 100755
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#!/bin/bash
set -e
source path.sh
gpus=0,1,2,3
stage=0
stop_stage=100
conf_path=conf/default.yaml
train_output_path=exp/default
ckpt_name=snapshot_iter_153.pdz
add_blank=true
ref_audio_dir=ref_audio
src_audio_path=''
# not include ".pdparams" here
ge2e_ckpt_path=./ge2e_ckpt_0.3/step-3000000
# include ".pdparams" here
ge2e_params_path=${ge2e_ckpt_path}.pdparams
# with the following command, you can choose the stage range you want to run
# such as `./run.sh --stage 0 --stop-stage 0`
# this can not be mixed use with `$1`, `$2` ...
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# prepare data
CUDA_VISIBLE_DEVICES=${gpus} ./local/preprocess.sh ${conf_path} ${add_blank} ${ge2e_ckpt_path} || exit -1
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
CUDA_VISIBLE_DEVICES=${gpus} ./local/voice_cloning.sh ${conf_path} ${train_output_path} ${ckpt_name} \
${ge2e_params_path} ${add_blank} ${ref_audio_dir} ${src_audio_path} || exit -1
fi
examples/aishell3/vits/README.md 0 → 100644
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# VITS with AISHELL-3
This example contains code used to train a [VITS](https://arxiv.org/abs/2106.06103) model with [AISHELL-3](http://www.aishelltech.com/aishell_3).
AISHELL-3 is a large-scale and high-fidelity multi-speaker Mandarin speech corpus that could be used to train multi-speaker Text-to-Speech (TTS) systems.
We use AISHELL-3 to train a multi-speaker VITS model here.
## Dataset
### Download and Extract
Download AISHELL-3 from it's [Official Website](http://www.aishelltech.com/aishell_3) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/data_aishell3`.
### Get MFA Result and Extract
We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get phonemes for VITS, the durations of MFA are not needed here.
You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) (use MFA1.x now) of our repo.
## Get Started
Assume the path to the dataset is `~/datasets/data_aishell3`.
Assume the path to the MFA result of AISHELL-3 is `./aishell3_alignment_tone`.
Run the command below to
1. **source path**.
2. preprocess the dataset.
3. train the model.
4. synthesize wavs.
- synthesize waveform from `metadata.jsonl`.
- synthesize waveform from a text file.
```bash
./run.sh
```
You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, running the following command will only preprocess the dataset.
```bash
./run.sh --stage 0 --stop-stage 0
```
### Data Preprocessing
```bash
./local/preprocess.sh ${conf_path}
```
When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
```text
dump
├── dev
│   ├── norm
│   └── raw
├── phone_id_map.txt
├── speaker_id_map.txt
├── test
│   ├── norm
│   └── raw
└── train
├── feats_stats.npy
├── norm
└── raw
```
The dataset is split into 3 parts, namely `train`, `dev`, and` test`, each of which contains a `norm` and `raw` subfolder. The raw folder contains wave and linear spectrogram of each utterance, while the norm folder contains normalized ones. The statistics used to normalize features are computed from the training set, which is located in `dump/train/feats_stats.npy`.
Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains phones, text_lengths, feats, feats_lengths, the path of linear spectrogram features, the path of raw waves, speaker, and the id of each utterance.
### Model Training
```bash
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
```
`./local/train.sh` calls `${BIN_DIR}/train.py`.
Here's the complete help message.
```text
usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
[--ngpu NGPU] [--phones-dict PHONES_DICT]
[--speaker-dict SPEAKER_DICT] [--voice-cloning VOICE_CLONING]
Train a VITS model.
optional arguments:
-h, --help show this help message and exit
--config CONFIG config file to overwrite default config.
--train-metadata TRAIN_METADATA
training data.
--dev-metadata DEV_METADATA
dev data.
--output-dir OUTPUT_DIR
output dir.
--ngpu NGPU if ngpu == 0, use cpu.
--phones-dict PHONES_DICT
phone vocabulary file.
--speaker-dict SPEAKER_DICT
speaker id map file for multiple speaker model.
--voice-cloning VOICE_CLONING
whether training voice cloning model.
```
1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are saved in `checkpoints/` inside this directory.
4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
5. `--phones-dict` is the path of the phone vocabulary file.
6. `--speaker-dict` is the path of the speaker id map file when training a multi-speaker VITS.
### Synthesizing
`./local/synthesize.sh` calls `${BIN_DIR}/synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
```bash
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
```
```text
usage: synthesize.py [-h] [--config CONFIG] [--ckpt CKPT]
[--phones_dict PHONES_DICT] [--speaker_dict SPEAKER_DICT]
[--voice-cloning VOICE_CLONING] [--ngpu NGPU]
[--test_metadata TEST_METADATA] [--output_dir OUTPUT_DIR]
Synthesize with VITS
optional arguments:
-h, --help show this help message and exit
--config CONFIG Config of VITS.
--ckpt CKPT Checkpoint file of VITS.
--phones_dict PHONES_DICT
phone vocabulary file.
--speaker_dict SPEAKER_DICT
speaker id map file.
--voice-cloning VOICE_CLONING
whether training voice cloning model.
--ngpu NGPU if ngpu == 0, use cpu.
--test_metadata TEST_METADATA
test metadata.
--output_dir OUTPUT_DIR
output dir.
```
`./local/synthesize_e2e.sh` calls `${BIN_DIR}/synthesize_e2e.py`, which can synthesize waveform from text file.
```bash
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name}
```
```text
usage: synthesize_e2e.py [-h] [--config CONFIG] [--ckpt CKPT]
[--phones_dict PHONES_DICT]
[--speaker_dict SPEAKER_DICT] [--spk_id SPK_ID]
[--lang LANG]
[--inference_dir INFERENCE_DIR] [--ngpu NGPU]
[--text TEXT] [--output_dir OUTPUT_DIR]
Synthesize with VITS
optional arguments:
-h, --help show this help message and exit
--config CONFIG Config of VITS.
--ckpt CKPT Checkpoint file of VITS.
--phones_dict PHONES_DICT
phone vocabulary file.
--speaker_dict SPEAKER_DICT
speaker id map file.
--spk_id SPK_ID spk id for multi speaker acoustic model
--lang LANG Choose model language. zh or en
--inference_dir INFERENCE_DIR
dir to save inference models
--ngpu NGPU if ngpu == 0, use cpu.
--text TEXT text to synthesize, a 'utt_id sentence' pair per line.
--output_dir OUTPUT_DIR
output dir.
```
1. `--config`, `--ckpt`, `--phones_dict` and `--speaker_dict` are arguments for acoustic model, which correspond to the 3 files in the VITS pretrained model.
2. `--lang` is the model language, which can be `zh` or `en`.
3. `--test_metadata` should be the metadata file in the normalized subfolder of `test` in the `dump` folder.
4. `--text` is the text file, which contains sentences to synthesize.
5. `--output_dir` is the directory to save synthesized audio files.
6. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
<!-- TODO display these after we trained the model -->
<!--
## Pretrained Model
The pretrained model can be downloaded here:
- [vits_aishell3_ckpt_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/vits/vits_aishell3_ckpt_1.1.0.zip) (add_blank=true)
VITS checkpoint contains files listed below.
```text
vits_aishell3_ckpt_1.1.0
├── default.yaml # default config used to train vitx
├── phone_id_map.txt # phone vocabulary file when training vits
├── speaker_id_map.txt # speaker id map file when training a multi-speaker vits
└── snapshot_iter_333000.pdz # model parameters and optimizer states
```
ps: This ckpt is not good enough, a better result is training
You can use the following scripts to synthesize for `${BIN_DIR}/../sentences.txt` using pretrained VITS.
```bash
source path.sh
add_blank=true
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/synthesize_e2e.py \
--config=vits_aishell3_ckpt_1.1.0/default.yaml \
--ckpt=vits_aishell3_ckpt_1.1.0/snapshot_iter_333000.pdz \
--phones_dict=vits_aishell3_ckpt_1.1.0/phone_id_map.txt \
--speaker_dict=vits_aishell3_ckpt_1.1.0/speaker_id_map.txt \
--output_dir=exp/default/test_e2e \
--text=${BIN_DIR}/../sentences.txt \
--add-blank=${add_blank}
```
-->
examples/aishell3/vits/conf/default.yaml 0 → 100644
浏览文件 @ 43af22e4
# This configuration tested on 4 GPUs (V100) with 32GB GPU
# memory. It takes around 2 weeks to finish the training
# but 100k iters model should generate reasonable results.
###########################################################
# FEATURE EXTRACTION SETTING #
###########################################################
fs: 22050 # sr
n_fft: 1024 # FFT size (samples).
n_shift: 256 # Hop size (samples). 12.5ms
win_length: null # Window length (samples). 50ms
# If set to null, it will be the same as fft_size.
window: "hann" # Window function.
##########################################################
# TTS MODEL SETTING #
##########################################################
model:
# generator related
generator_type: vits_generator
generator_params:
hidden_channels: 192
global_channels: 256
segment_size: 32
text_encoder_attention_heads: 2
text_encoder_ffn_expand: 4
text_encoder_blocks: 6
text_encoder_positionwise_layer_type: "conv1d"
text_encoder_positionwise_conv_kernel_size: 3
text_encoder_positional_encoding_layer_type: "rel_pos"
text_encoder_self_attention_layer_type: "rel_selfattn"
text_encoder_activation_type: "swish"
text_encoder_normalize_before: True
text_encoder_dropout_rate: 0.1
text_encoder_positional_dropout_rate: 0.0
text_encoder_attention_dropout_rate: 0.1
use_macaron_style_in_text_encoder: True
use_conformer_conv_in_text_encoder: False
text_encoder_conformer_kernel_size: -1
decoder_kernel_size: 7
decoder_channels: 512
decoder_upsample_scales: [8, 8, 2, 2]
decoder_upsample_kernel_sizes: [16, 16, 4, 4]
decoder_resblock_kernel_sizes: [3, 7, 11]
decoder_resblock_dilations: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
use_weight_norm_in_decoder: True
posterior_encoder_kernel_size: 5
posterior_encoder_layers: 16
posterior_encoder_stacks: 1
posterior_encoder_base_dilation: 1
posterior_encoder_dropout_rate: 0.0
use_weight_norm_in_posterior_encoder: True
flow_flows: 4
flow_kernel_size: 5
flow_base_dilation: 1
flow_layers: 4
flow_dropout_rate: 0.0
use_weight_norm_in_flow: True
use_only_mean_in_flow: True
stochastic_duration_predictor_kernel_size: 3
stochastic_duration_predictor_dropout_rate: 0.5
stochastic_duration_predictor_flows: 4
stochastic_duration_predictor_dds_conv_layers: 3
# discriminator related
discriminator_type: hifigan_multi_scale_multi_period_discriminator
discriminator_params:
scales: 1
scale_downsample_pooling: "AvgPool1D"
scale_downsample_pooling_params:
kernel_size: 4
stride: 2
padding: 2
scale_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes: [15, 41, 5, 3]
channels: 128
max_downsample_channels: 1024
max_groups: 16
bias: True
downsample_scales: [2, 2, 4, 4, 1]
nonlinear_activation: "leakyrelu"
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: True
use_spectral_norm: False
follow_official_norm: False
periods: [2, 3, 5, 7, 11]
period_discriminator_params:
in_channels: 1
out_channels: 1
kernel_sizes: [5, 3]
channels: 32
downsample_scales: [3, 3, 3, 3, 1]
max_downsample_channels: 1024
bias: True
nonlinear_activation: "leakyrelu"
nonlinear_activation_params:
negative_slope: 0.1
use_weight_norm: True
use_spectral_norm: False
# others
sampling_rate: 22050 # needed in the inference for saving wav
cache_generator_outputs: True # whether to cache generator outputs in the training
###########################################################
# LOSS SETTING #
###########################################################
# loss function related
generator_adv_loss_params:
average_by_discriminators: False # whether to average loss value by #discriminators
loss_type: mse # loss type, "mse" or "hinge"
discriminator_adv_loss_params:
average_by_discriminators: False # whether to average loss value by #discriminators
loss_type: mse # loss type, "mse" or "hinge"
feat_match_loss_params:
average_by_discriminators: False # whether to average loss value by #discriminators
average_by_layers: False # whether to average loss value by #layers of each discriminator
include_final_outputs: True # whether to include final outputs for loss calculation
mel_loss_params:
fs: 22050 # must be the same as the training data
fft_size: 1024 # fft points
hop_size: 256 # hop size
win_length: null # window length
window: hann # window type
num_mels: 80 # number of Mel basis
fmin: 0 # minimum frequency for Mel basis
fmax: null # maximum frequency for Mel basis
log_base: null # null represent natural log
###########################################################
# ADVERSARIAL LOSS SETTING #
###########################################################
lambda_adv: 1.0 # loss scaling coefficient for adversarial loss
lambda_mel: 45.0 # loss scaling coefficient for Mel loss
lambda_feat_match: 2.0 # loss scaling coefficient for feat match loss
lambda_dur: 1.0 # loss scaling coefficient for duration loss
lambda_kl: 1.0 # loss scaling coefficient for KL divergence loss
# others
sampling_rate: 22050 # needed in the inference for saving wav
cache_generator_outputs: True # whether to cache generator outputs in the training
###########################################################
# DATA LOADER SETTING #
###########################################################
batch_size: 50 # Batch size.
num_workers: 4 # Number of workers in DataLoader.
##########################################################
# OPTIMIZER & SCHEDULER SETTING #
##########################################################
# optimizer setting for generator
generator_optimizer_params:
beta1: 0.8
beta2: 0.99
epsilon: 1.0e-9
weight_decay: 0.0
generator_scheduler: exponential_decay
generator_scheduler_params:
learning_rate: 2.0e-4
gamma: 0.999875
# optimizer setting for discriminator
discriminator_optimizer_params:
beta1: 0.8
beta2: 0.99
epsilon: 1.0e-9
weight_decay: 0.0
discriminator_scheduler: exponential_decay
discriminator_scheduler_params:
learning_rate: 2.0e-4
gamma: 0.999875
generator_first: False # whether to start updating generator first
##########################################################
# OTHER TRAINING SETTING #
##########################################################
num_snapshots: 10 # max number of snapshots to keep while training
train_max_steps: 350000 # Number of training steps. == total_iters / ngpus, total_iters = 1000000
save_interval_steps: 1000 # Interval steps to save checkpoint.
eval_interval_steps: 250 # Interval steps to evaluate the network.
seed: 777 # random seed number
examples/aishell3/vits/local/preprocess.sh 0 → 100755
浏览文件 @ 43af22e4
#!/bin/bash
stage=0
stop_stage=100
config_path=$1
add_blank=$2
# copy from tts3/preprocess
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# get durations from MFA's result
echo "Generate durations.txt from MFA results ..."
python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
--inputdir=./aishell3_alignment_tone \
--output durations.txt \
--config=${config_path}
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# extract features
echo "Extract features ..."
python3 ${BIN_DIR}/preprocess.py \
--dataset=aishell3 \
--rootdir=~/datasets/data_aishell3/ \
--dumpdir=dump \
--dur-file=durations.txt \
--config=${config_path} \
--num-cpu=20 \
--cut-sil=True
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# get features' stats(mean and std)
echo "Get features' stats ..."
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
--metadata=dump/train/raw/metadata.jsonl \
--field-name="feats"
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# normalize and covert phone/speaker to id, dev and test should use train's stats
echo "Normalize ..."
python3 ${BIN_DIR}/normalize.py \
--metadata=dump/train/raw/metadata.jsonl \
--dumpdir=dump/train/norm \
--feats-stats=dump/train/feats_stats.npy \
--phones-dict=dump/phone_id_map.txt \
--speaker-dict=dump/speaker_id_map.txt \
--add-blank=${add_blank} \
--skip-wav-copy
python3 ${BIN_DIR}/normalize.py \
--metadata=dump/dev/raw/metadata.jsonl \
--dumpdir=dump/dev/norm \
--feats-stats=dump/train/feats_stats.npy \
--phones-dict=dump/phone_id_map.txt \
--speaker-dict=dump/speaker_id_map.txt \
--add-blank=${add_blank} \
--skip-wav-copy
python3 ${BIN_DIR}/normalize.py \
--metadata=dump/test/raw/metadata.jsonl \
--dumpdir=dump/test/norm \
--feats-stats=dump/train/feats_stats.npy \
--phones-dict=dump/phone_id_map.txt \
--speaker-dict=dump/speaker_id_map.txt \
--add-blank=${add_blank} \
--skip-wav-copy
fi
examples/aishell3/vits/local/synthesize.sh 0 → 100755
浏览文件 @ 43af22e4
#!/bin/bash
config_path=$1
train_output_path=$2
ckpt_name=$3
stage=0
stop_stage=0
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/synthesize.py \
--config=${config_path} \
--ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--phones_dict=dump/phone_id_map.txt \
--speaker_dict=dump/speaker_id_map.txt \
--test_metadata=dump/test/norm/metadata.jsonl \
--output_dir=${train_output_path}/test
fi
examples/aishell3/vits/local/synthesize_e2e.sh 0 → 100755
浏览文件 @ 43af22e4
#!/bin/bash
config_path=$1
train_output_path=$2
ckpt_name=$3
add_blank=$4
stage=0
stop_stage=0
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/synthesize_e2e.py \
--config=${config_path} \
--ckpt=${train_output_path}/checkpoints/${ckpt_name} \
--phones_dict=dump/phone_id_map.txt \
--speaker_dict=dump/speaker_id_map.txt \
--spk_id=0 \
--output_dir=${train_output_path}/test_e2e \
--text=${BIN_DIR}/../sentences.txt \
--add-blank=${add_blank}
fi
examples/aishell3/vits/local/train.sh 0 → 100755
浏览文件 @ 43af22e4
#!/bin/bash
config_path=$1
train_output_path=$2
# install monotonic_align
cd ${MAIN_ROOT}/paddlespeech/t2s/models/vits/monotonic_align
python3 setup.py build_ext --inplace
cd -
python3 ${BIN_DIR}/train.py \
--train-metadata=dump/train/norm/metadata.jsonl \
--dev-metadata=dump/dev/norm/metadata.jsonl \
--config=${config_path} \
--output-dir=${train_output_path} \
--ngpu=4 \
--phones-dict=dump/phone_id_map.txt \
--speaker-dict=dump/speaker_id_map.txt
examples/aishell3/vits/path.sh 0 → 100755
浏览文件 @ 43af22e4
#!/bin/bash
export MAIN_ROOT=`realpath ${PWD}/../../../`
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
export LC_ALL=C
export PYTHONDONTWRITEBYTECODE=1
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
export PYTHONIOENCODING=UTF-8
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
MODEL=vits
export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}
\ No newline at end of file
examples/aishell3/vits/run.sh 0 → 100755
浏览文件 @ 43af22e4
#!/bin/bash
set -e
source path.sh
gpus=0,1,2,3
stage=0
stop_stage=100
conf_path=conf/default.yaml
train_output_path=exp/default
ckpt_name=snapshot_iter_153.pdz
add_blank=true
# with the following command, you can choose the stage range you want to run
# such as `./run.sh --stage 0 --stop-stage 0`
# this can not be mixed use with `$1`, `$2` ...
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# prepare data
./local/preprocess.sh ${conf_path} ${add_blank}|| exit -1
fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name} ${add_blank}|| exit -1
fi
examples/csmsc/tts2/run.sh
浏览文件 @ 43af22e4
...@@ -46,8 +46,8 @@ fi ...@@ -46,8 +46,8 @@ fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# install paddle2onnx # install paddle2onnx
version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
if [[ -z "$version" || ${version} != '0.9.8' ]]; then if [[ -z "$version" || ${version} != '1.0.0' ]]; then
pip install paddle2onnx==0.9.8 pip install paddle2onnx==1.0.0
fi fi
./local/paddle2onnx.sh ${train_output_path} inference inference_onnx speedyspeech_csmsc ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx speedyspeech_csmsc
# considering the balance between speed and quality, we recommend that you use hifigan as vocoder # considering the balance between speed and quality, we recommend that you use hifigan as vocoder
......
examples/csmsc/tts3/run.sh
浏览文件 @ 43af22e4
...@@ -46,8 +46,8 @@ fi ...@@ -46,8 +46,8 @@ fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# install paddle2onnx # install paddle2onnx
version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
if [[ -z "$version" || ${version} != '0.9.8' ]]; then if [[ -z "$version" || ${version} != '1.0.0' ]]; then
pip install paddle2onnx==0.9.8 pip install paddle2onnx==1.0.0
fi fi
./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_csmsc ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_csmsc
# considering the balance between speed and quality, we recommend that you use hifigan as vocoder # considering the balance between speed and quality, we recommend that you use hifigan as vocoder
......
examples/csmsc/tts3/run_cnndecoder.sh
浏览文件 @ 43af22e4
...@@ -59,8 +59,8 @@ fi ...@@ -59,8 +59,8 @@ fi
if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
# install paddle2onnx # install paddle2onnx
version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
if [[ -z "$version" || ${version} != '0.9.8' ]]; then if [[ -z "$version" || ${version} != '1.0.0' ]]; then
pip install paddle2onnx==0.9.8 pip install paddle2onnx==1.0.0
fi fi
./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_csmsc ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_csmsc
# considering the balance between speed and quality, we recommend that you use hifigan as vocoder # considering the balance between speed and quality, we recommend that you use hifigan as vocoder
...@@ -79,8 +79,8 @@ fi ...@@ -79,8 +79,8 @@ fi
if [ ${stage} -le 9 ] && [ ${stop_stage} -ge 9 ]; then if [ ${stage} -le 9 ] && [ ${stop_stage} -ge 9 ]; then
# install paddle2onnx # install paddle2onnx
version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
if [[ -z "$version" || ${version} != '0.9.8' ]]; then if [[ -z "$version" || ${version} != '1.0.0' ]]; then
pip install paddle2onnx==0.9.8 pip install paddle2onnx==1.0.0
fi fi
# streaming acoustic model # streaming acoustic model
./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming fastspeech2_csmsc_am_encoder_infer ./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming fastspeech2_csmsc_am_encoder_infer
......
examples/csmsc/vits/run.sh
浏览文件 @ 43af22e4
...@@ -3,7 +3,7 @@ ...@@ -3,7 +3,7 @@
set -e set -e
source path.sh source path.sh
gpus=0,1 gpus=0,1,2,3
stage=0 stage=0
stop_stage=100 stop_stage=100
......
examples/iwslt2012/punc0/local/preprocess.py
浏览文件 @ 43af22e4
import argparse import argparse
import os
def process_sentence(line): def process_sentence(line):
if line == '': return '' if line == '':
res = line[0] return ''
for i in range(1, len(line)): res = line[0]
res += (' ' + line[i]) for i in range(1, len(line)):
return res res += (' ' + line[i])
return res
if __name__ == "__main__": if __name__ == "__main__":
paser = argparse.ArgumentParser(description = "Input filename") paser = argparse.ArgumentParser(description="Input filename")
paser.add_argument('-input_file') paser.add_argument('-input_file')
paser.add_argument('-output_file') paser.add_argument('-output_file')
sentence_cnt = 0 sentence_cnt = 0
args = paser.parse_args() args = paser.parse_args()
with open(args.input_file, 'r') as f: with open(args.input_file, 'r') as f:
with open(args.output_file, 'w') as write_f: with open(args.output_file, 'w') as write_f:
while True: while True:
line = f.readline() line = f.readline()
if line: if line:
sentence_cnt += 1 sentence_cnt += 1
write_f.write(process_sentence(line)) write_f.write(process_sentence(line))
else: else:
break break
print('preprocess over') print('preprocess over')
print('total sentences number:', sentence_cnt) print('total sentences number:', sentence_cnt)
examples/ljspeech/tts3/run.sh
浏览文件 @ 43af22e4
...@@ -46,8 +46,8 @@ fi ...@@ -46,8 +46,8 @@ fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# install paddle2onnx # install paddle2onnx
version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
if [[ -z "$version" || ${version} != '0.9.8' ]]; then if [[ -z "$version" || ${version} != '1.0.0' ]]; then
pip install paddle2onnx==0.9.8 pip install paddle2onnx==1.0.0
fi fi
./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_ljspeech ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_ljspeech
# considering the balance between speed and quality, we recommend that you use hifigan as vocoder # considering the balance between speed and quality, we recommend that you use hifigan as vocoder
......
examples/other/g2p/README.md
浏览文件 @ 43af22e4
...@@ -12,13 +12,13 @@ Run the command below to get the results of the test. ...@@ -12,13 +12,13 @@ Run the command below to get the results of the test.
./run.sh ./run.sh
``` ```
The `avg WER` of g2p is: 0.024169315564825305 The `avg WER` of g2p is: 0.024075726733983775
```text ```text
,--------------------------------------------------------------------. ,--------------------------------------------------------------------.
| ./exp/g2p/text.g2p | | ./exp/g2p/text.g2p |
|--------------------------------------------------------------------| |--------------------------------------------------------------------|
| SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err | | SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err |
| Sum/Avg| 9996 299181 | 97.6 2.4 0.0 0.0 2.4 49.2 | | Sum/Avg| 9996 299181 | 97.6 2.4 0.0 0.0 2.4 49.0 |
`--------------------------------------------------------------------' `--------------------------------------------------------------------'
``` ```
examples/other/tts_finetune/tts3/finetune.py
浏览文件 @ 43af22e4
...@@ -17,15 +17,14 @@ from pathlib import Path ...@@ -17,15 +17,14 @@ from pathlib import Path
from typing import Union from typing import Union
import yaml import yaml
from paddle import distributed as dist
from yacs.config import CfgNode
from paddlespeech.t2s.exps.fastspeech2.train import train_sp
from local.check_oov import get_check_result from local.check_oov import get_check_result
from local.extract import extract_feature from local.extract import extract_feature
from local.label_process import get_single_label from local.label_process import get_single_label
from local.prepare_env import generate_finetune_env from local.prepare_env import generate_finetune_env
from paddle import distributed as dist
from yacs.config import CfgNode
from paddlespeech.t2s.exps.fastspeech2.train import train_sp
from utils.gen_duration_from_textgrid import gen_duration_from_textgrid from utils.gen_duration_from_textgrid import gen_duration_from_textgrid
DICT_EN = 'tools/aligner/cmudict-0.7b' DICT_EN = 'tools/aligner/cmudict-0.7b'
......
examples/vctk/tts3/run.sh
浏览文件 @ 43af22e4
...@@ -44,8 +44,8 @@ fi ...@@ -44,8 +44,8 @@ fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# install paddle2onnx # install paddle2onnx
version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
if [[ -z "$version" || ${version} != '0.9.8' ]]; then if [[ -z "$version" || ${version} != '1.0.0' ]]; then
pip install paddle2onnx==0.9.8 pip install paddle2onnx==1.0.0
fi fi
./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_vctk ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_vctk
# considering the balance between speed and quality, we recommend that you use hifigan as vocoder # considering the balance between speed and quality, we recommend that you use hifigan as vocoder
......
examples/zh_en_tts/tts3/run.sh
浏览文件 @ 43af22e4
...@@ -47,8 +47,8 @@ fi ...@@ -47,8 +47,8 @@ fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# install paddle2onnx # install paddle2onnx
version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
if [[ -z "$version" || ${version} != '0.9.8' ]]; then if [[ -z "$version" || ${version} != '1.0.0' ]]; then
pip install paddle2onnx==0.9.8 pip install paddle2onnx==1.0.0
fi fi
./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_mix ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_mix
# considering the balance between speed and quality, we recommend that you use hifigan as vocoder # considering the balance between speed and quality, we recommend that you use hifigan as vocoder
......
paddlespeech/__init__.py
浏览文件 @ 43af22e4
...@@ -14,5 +14,3 @@ ...@@ -14,5 +14,3 @@
import _locale import _locale
_locale._getdefaultlocale = (lambda *args: ['en_US', 'utf8']) _locale._getdefaultlocale = (lambda *args: ['en_US', 'utf8'])
paddlespeech/audio/__init__.py
浏览文件 @ 43af22e4
...@@ -14,12 +14,12 @@ ...@@ -14,12 +14,12 @@
from . import compliance from . import compliance
from . import datasets from . import datasets
from . import features from . import features
from . import text
from . import transform
from . import streamdata
from . import functional from . import functional
from . import io from . import io
from . import metric from . import metric
from . import sox_effects from . import sox_effects
from . import streamdata
from . import text
from . import transform
from .backends import load from .backends import load
from .backends import save from .backends import save
paddlespeech/audio/streamdata/__init__.py
浏览文件 @ 43af22e4
...@@ -4,67 +4,66 @@ ...@@ -4,67 +4,66 @@
# Modified from https://github.com/webdataset/webdataset # Modified from https://github.com/webdataset/webdataset
# #
# flake8: noqa # flake8: noqa
from .cache import cached_tarfile_samples
from .cache import ( from .cache import cached_tarfile_to_samples
cached_tarfile_samples, from .cache import lru_cleanup
cached_tarfile_to_samples, from .cache import pipe_cleaner
lru_cleanup, from .compat import FluidWrapper
pipe_cleaner, from .compat import WebDataset
) from .compat import WebLoader
from .compat import WebDataset, WebLoader, FluidWrapper from .extradatasets import MockDataset
from .extradatasets import MockDataset, with_epoch, with_length from .extradatasets import with_epoch
from .filters import ( from .extradatasets import with_length
associate, from .filters import associate
batched, from .filters import audio_cmvn
decode, from .filters import audio_compute_fbank
detshuffle, from .filters import audio_data_filter
extract_keys, from .filters import audio_padding
getfirst, from .filters import audio_resample
info, from .filters import audio_spec_aug
map, from .filters import audio_tokenize
map_dict, from .filters import batched
map_tuple, from .filters import decode
pipelinefilter, from .filters import detshuffle
rename, from .filters import extract_keys
rename_keys, from .filters import getfirst
audio_resample, from .filters import info
select, from .filters import map
shuffle, from .filters import map_dict
slice, from .filters import map_tuple
to_tuple, from .filters import pipelinefilter
transform_with, from .filters import placeholder
unbatched, from .filters import rename
xdecode, from .filters import rename_keys
audio_data_filter, from .filters import select
audio_tokenize, from .filters import shuffle
audio_resample, from .filters import slice
audio_compute_fbank, from .filters import sort
audio_spec_aug, from .filters import to_tuple
sort, from .filters import transform_with
audio_padding, from .filters import unbatched
audio_cmvn, from .filters import xdecode
placeholder, from .handlers import ignore_and_continue
) from .handlers import ignore_and_stop
from .handlers import ( from .handlers import reraise_exception
ignore_and_continue, from .handlers import warn_and_continue
ignore_and_stop, from .handlers import warn_and_stop
reraise_exception, from .mix import RandomMix
warn_and_continue, from .mix import RoundRobin
warn_and_stop,
)
from .pipeline import DataPipeline from .pipeline import DataPipeline
from .shardlists import ( from .shardlists import MultiShardSample
MultiShardSample, from .shardlists import non_empty
ResampledShards, from .shardlists import resampled
SimpleShardList, from .shardlists import ResampledShards
non_empty, from .shardlists import shardspec
resampled, from .shardlists import SimpleShardList
shardspec, from .shardlists import single_node_only
single_node_only, from .shardlists import split_by_node
split_by_node, from .shardlists import split_by_worker
split_by_worker, from .tariterators import tarfile_samples
) from .tariterators import tarfile_to_samples
from .tariterators import tarfile_samples, tarfile_to_samples from .utils import PipelineStage
from .utils import PipelineStage, repeatedly from .utils import repeatedly
from .writer import ShardWriter, TarWriter, numpy_dumps from .writer import numpy_dumps
from .mix import RandomMix, RoundRobin from .writer import ShardWriter
from .writer import TarWriter
paddlespeech/audio/streamdata/autodecode.py
浏览文件 @ 43af22e4
...@@ -5,18 +5,19 @@ ...@@ -5,18 +5,19 @@
# See the LICENSE file for licensing terms (BSD-style). # See the LICENSE file for licensing terms (BSD-style).
# Modified from https://github.com/webdataset/webdataset # Modified from https://github.com/webdataset/webdataset
# #
"""Automatically decode webdataset samples.""" """Automatically decode webdataset samples."""
import io
import io, json, os, pickle, re, tempfile import json
import os
import pickle
import re
import tempfile
from functools import partial from functools import partial
import numpy as np import numpy as np
"""Extensions passed on to the image decoder.""" """Extensions passed on to the image decoder."""
image_extensions = "jpg jpeg png ppm pgm pbm pnm".split() image_extensions = "jpg jpeg png ppm pgm pbm pnm".split()
################################################################ ################################################################
# handle basic datatypes # handle basic datatypes
################################################################ ################################################################
...@@ -128,7 +129,7 @@ def call_extension_handler(key, data, f, extensions): ...@@ -128,7 +129,7 @@ def call_extension_handler(key, data, f, extensions):
target = target.split(".") target = target.split(".")
if len(target) > len(extension): if len(target) > len(extension):
continue continue
if extension[-len(target) :] == target: if extension[-len(target):] == target:
return f(data) return f(data)
return None return None
...@@ -268,7 +269,6 @@ def imagehandler(imagespec, extensions=image_extensions): ...@@ -268,7 +269,6 @@ def imagehandler(imagespec, extensions=image_extensions):
################################################################ ################################################################
# torch video # torch video
################################################################ ################################################################
''' '''
def torch_video(key, data): def torch_video(key, data):
"""Decode video using the torchvideo library. """Decode video using the torchvideo library.
...@@ -289,7 +289,6 @@ def torch_video(key, data): ...@@ -289,7 +289,6 @@ def torch_video(key, data):
return torchvision.io.read_video(fname, pts_unit="sec") return torchvision.io.read_video(fname, pts_unit="sec")
''' '''
################################################################ ################################################################
# paddlespeech.audio # paddlespeech.audio
################################################################ ################################################################
...@@ -359,7 +358,6 @@ def gzfilter(key, data): ...@@ -359,7 +358,6 @@ def gzfilter(key, data):
# decode entire training amples # decode entire training amples
################################################################ ################################################################
default_pre_handlers = [gzfilter] default_pre_handlers = [gzfilter]
default_post_handlers = [basichandlers] default_post_handlers = [basichandlers]
...@@ -387,7 +385,8 @@ class Decoder: ...@@ -387,7 +385,8 @@ class Decoder:
pre = default_pre_handlers pre = default_pre_handlers
if post is None: if post is None:
post = default_post_handlers post = default_post_handlers
assert all(callable(h) for h in handlers), f"one of {handlers} not callable" assert all(callable(h)
for h in handlers), f"one of {handlers} not callable"
assert all(callable(h) for h in pre), f"one of {pre} not callable" assert all(callable(h) for h in pre), f"one of {pre} not callable"
assert all(callable(h) for h in post), f"one of {post} not callable" assert all(callable(h) for h in post), f"one of {post} not callable"
self.handlers = pre + handlers + post self.handlers = pre + handlers + post
......
paddlespeech/audio/streamdata/cache.py
浏览文件 @ 43af22e4
...@@ -2,7 +2,10 @@ ...@@ -2,7 +2,10 @@
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# See the LICENSE file for licensing terms (BSD-style). # See the LICENSE file for licensing terms (BSD-style).
# Modified from https://github.com/webdataset/webdataset # Modified from https://github.com/webdataset/webdataset
import itertools, os, random, re, sys import os
import random
import re
import sys
from urllib.parse import urlparse from urllib.parse import urlparse
from . import filters from . import filters
...@@ -40,7 +43,7 @@ def lru_cleanup(cache_dir, cache_size, keyfn=os.path.getctime, verbose=False): ...@@ -40,7 +43,7 @@ def lru_cleanup(cache_dir, cache_size, keyfn=os.path.getctime, verbose=False):
os.remove(fname) os.remove(fname)
def download(url, dest, chunk_size=1024 ** 2, verbose=False): def download(url, dest, chunk_size=1024**2, verbose=False):
"""Download a file from `url` to `dest`.""" """Download a file from `url` to `dest`."""
temp = dest + f".temp{os.getpid()}" temp = dest + f".temp{os.getpid()}"
with gopen.gopen(url) as stream: with gopen.gopen(url) as stream:
...@@ -65,12 +68,11 @@ def pipe_cleaner(spec): ...@@ -65,12 +68,11 @@ def pipe_cleaner(spec):
def get_file_cached( def get_file_cached(
spec, spec,
cache_size=-1, cache_size=-1,
cache_dir=None, cache_dir=None,
url_to_name=pipe_cleaner, url_to_name=pipe_cleaner,
verbose=False, verbose=False, ):
):
if cache_size == -1: if cache_size == -1:
cache_size = default_cache_size cache_size = default_cache_size
if cache_dir is None: if cache_dir is None:
...@@ -107,15 +109,14 @@ verbose_cache = int(os.environ.get("WDS_VERBOSE_CACHE", "0")) ...@@ -107,15 +109,14 @@ verbose_cache = int(os.environ.get("WDS_VERBOSE_CACHE", "0"))
def cached_url_opener( def cached_url_opener(
data, data,
handler=reraise_exception, handler=reraise_exception,
cache_size=-1, cache_size=-1,
cache_dir=None, cache_dir=None,
url_to_name=pipe_cleaner, url_to_name=pipe_cleaner,
validator=check_tar_format, validator=check_tar_format,
verbose=False, verbose=False,
always=False, always=False, ):
):
"""Given a stream of url names (packaged in `dict(url=url)`), yield opened streams.""" """Given a stream of url names (packaged in `dict(url=url)`), yield opened streams."""
verbose = verbose or verbose_cache verbose = verbose or verbose_cache
for sample in data: for sample in data:
...@@ -132,8 +133,7 @@ def cached_url_opener( ...@@ -132,8 +133,7 @@ def cached_url_opener(
cache_size=cache_size, cache_size=cache_size,
cache_dir=cache_dir, cache_dir=cache_dir,
url_to_name=url_to_name, url_to_name=url_to_name,
verbose=verbose, verbose=verbose, )
)
if verbose: if verbose:
print("# opening %s" % dest, file=sys.stderr) print("# opening %s" % dest, file=sys.stderr)
assert os.path.exists(dest) assert os.path.exists(dest)
...@@ -143,9 +143,8 @@ def cached_url_opener( ...@@ -143,9 +143,8 @@ def cached_url_opener(
data = f.read(200) data = f.read(200)
os.remove(dest) os.remove(dest)
raise ValueError( raise ValueError(
"%s (%s) is not a tar archive, but a %s, contains %s" "%s (%s) is not a tar archive, but a %s, contains %s" %
% (dest, url, ftype, repr(data)) (dest, url, ftype, repr(data)))
)
try: try:
stream = open(dest, "rb") stream = open(dest, "rb")
sample.update(stream=stream) sample.update(stream=stream)
...@@ -158,7 +157,7 @@ def cached_url_opener( ...@@ -158,7 +157,7 @@ def cached_url_opener(
continue continue
raise exn raise exn
except Exception as exn: except Exception as exn:
exn.args = exn.args + (url,) exn.args = exn.args + (url, )
if handler(exn): if handler(exn):
continue continue
else: else:
...@@ -166,14 +165,13 @@ def cached_url_opener( ...@@ -166,14 +165,13 @@ def cached_url_opener(
def cached_tarfile_samples( def cached_tarfile_samples(
src, src,
handler=reraise_exception, handler=reraise_exception,
cache_size=-1, cache_size=-1,
cache_dir=None, cache_dir=None,
verbose=False, verbose=False,
url_to_name=pipe_cleaner, url_to_name=pipe_cleaner,
always=False, always=False, ):
):
streams = cached_url_opener( streams = cached_url_opener(
src, src,
handler=handler, handler=handler,
...@@ -181,8 +179,7 @@ def cached_tarfile_samples( ...@@ -181,8 +179,7 @@ def cached_tarfile_samples(
cache_dir=cache_dir, cache_dir=cache_dir,
verbose=verbose, verbose=verbose,
url_to_name=url_to_name, url_to_name=url_to_name,
always=always, always=always, )
)
samples = tar_file_and_group_expander(streams, handler=handler) samples = tar_file_and_group_expander(streams, handler=handler)
return samples return samples
......
paddlespeech/audio/streamdata/compat.py
浏览文件 @ 43af22e4
...@@ -2,17 +2,17 @@ ...@@ -2,17 +2,17 @@
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# See the LICENSE file for licensing terms (BSD-style). # See the LICENSE file for licensing terms (BSD-style).
# Modified from https://github.com/webdataset/webdataset # Modified from https://github.com/webdataset/webdataset
from dataclasses import dataclass import yaml
from itertools import islice
from typing import List
import braceexpand, yaml
from . import autodecode from . import autodecode
from . import cache, filters, shardlists, tariterators from . import cache
from . import filters
from . import shardlists
from . import tariterators
from .filters import reraise_exception from .filters import reraise_exception
from .paddle_utils import DataLoader
from .paddle_utils import IterableDataset
from .pipeline import DataPipeline from .pipeline import DataPipeline
from .paddle_utils import DataLoader, IterableDataset
class FluidInterface: class FluidInterface:
...@@ -26,7 +26,8 @@ class FluidInterface: ...@@ -26,7 +26,8 @@ class FluidInterface:
return self.compose(filters.unbatched()) return self.compose(filters.unbatched())
def listed(self, batchsize, partial=True): def listed(self, batchsize, partial=True):
return self.compose(filters.batched(), batchsize=batchsize, collation_fn=None) return self.compose(
filters.batched(), batchsize=batchsize, collation_fn=None)
def unlisted(self): def unlisted(self):
return self.compose(filters.unlisted()) return self.compose(filters.unlisted())
...@@ -43,9 +44,19 @@ class FluidInterface: ...@@ -43,9 +44,19 @@ class FluidInterface:
def map(self, f, handler=reraise_exception): def map(self, f, handler=reraise_exception):
return self.compose(filters.map(f, handler=handler)) return self.compose(filters.map(f, handler=handler))
def decode(self, *args, pre=None, post=None, only=None, partial=False, handler=reraise_exception): def decode(self,
handlers = [autodecode.ImageHandler(x) if isinstance(x, str) else x for x in args] *args,
decoder = autodecode.Decoder(handlers, pre=pre, post=post, only=only, partial=partial) pre=None,
post=None,
only=None,
partial=False,
handler=reraise_exception):
handlers = [
autodecode.ImageHandler(x) if isinstance(x, str) else x
for x in args
]
decoder = autodecode.Decoder(
handlers, pre=pre, post=post, only=only, partial=partial)
return self.map(decoder, handler=handler) return self.map(decoder, handler=handler)
def map_dict(self, handler=reraise_exception, **kw): def map_dict(self, handler=reraise_exception, **kw):
...@@ -80,12 +91,12 @@ class FluidInterface: ...@@ -80,12 +91,12 @@ class FluidInterface:
def audio_data_filter(self, *args, **kw): def audio_data_filter(self, *args, **kw):
return self.compose(filters.audio_data_filter(*args, **kw)) return self.compose(filters.audio_data_filter(*args, **kw))
def audio_tokenize(self, *args, **kw): def audio_tokenize(self, *args, **kw):
return self.compose(filters.audio_tokenize(*args, **kw)) return self.compose(filters.audio_tokenize(*args, **kw))
def resample(self, *args, **kw): def resample(self, *args, **kw):
return self.compose(filters.resample(*args, **kw)) return self.compose(filters.resample(*args, **kw))
def audio_compute_fbank(self, *args, **kw): def audio_compute_fbank(self, *args, **kw):
return self.compose(filters.audio_compute_fbank(*args, **kw)) return self.compose(filters.audio_compute_fbank(*args, **kw))
...@@ -102,27 +113,28 @@ class FluidInterface: ...@@ -102,27 +113,28 @@ class FluidInterface:
def audio_cmvn(self, cmvn_file): def audio_cmvn(self, cmvn_file):
return self.compose(filters.audio_cmvn(cmvn_file)) return self.compose(filters.audio_cmvn(cmvn_file))
class WebDataset(DataPipeline, FluidInterface): class WebDataset(DataPipeline, FluidInterface):
"""Small fluid-interface wrapper for DataPipeline.""" """Small fluid-interface wrapper for DataPipeline."""
def __init__( def __init__(
self, self,
urls, urls,
handler=reraise_exception, handler=reraise_exception,
resampled=False, resampled=False,
repeat=False, repeat=False,
shardshuffle=None, shardshuffle=None,
cache_size=0, cache_size=0,
cache_dir=None, cache_dir=None,
detshuffle=False, detshuffle=False,
nodesplitter=shardlists.single_node_only, nodesplitter=shardlists.single_node_only,
verbose=False, verbose=False, ):
):
super().__init__() super().__init__()
if isinstance(urls, IterableDataset): if isinstance(urls, IterableDataset):
assert not resampled assert not resampled
self.append(urls) self.append(urls)
elif isinstance(urls, str) and (urls.endswith(".yaml") or urls.endswith(".yml")): elif isinstance(urls, str) and (urls.endswith(".yaml") or
urls.endswith(".yml")):
with (open(urls)) as stream: with (open(urls)) as stream:
spec = yaml.safe_load(stream) spec = yaml.safe_load(stream)
assert "datasets" in spec assert "datasets" in spec
...@@ -152,9 +164,7 @@ class WebDataset(DataPipeline, FluidInterface): ...@@ -152,9 +164,7 @@ class WebDataset(DataPipeline, FluidInterface):
handler=handler, handler=handler,
verbose=verbose, verbose=verbose,
cache_size=cache_size, cache_size=cache_size,
cache_dir=cache_dir, cache_dir=cache_dir, ))
)
)
class FluidWrapper(DataPipeline, FluidInterface): class FluidWrapper(DataPipeline, FluidInterface):
......
paddlespeech/audio/streamdata/extradatasets.py
浏览文件 @ 43af22e4
...@@ -5,20 +5,10 @@ ...@@ -5,20 +5,10 @@
# See the LICENSE file for licensing terms (BSD-style). # See the LICENSE file for licensing terms (BSD-style).
# Modified from https://github.com/webdataset/webdataset # Modified from https://github.com/webdataset/webdataset
# #
"""Train PyTorch models directly from POSIX tar archive. """Train PyTorch models directly from POSIX tar archive.
Code works locally or over HTTP connections. Code works locally or over HTTP connections.
""" """
import itertools as itt
import os
import random
import sys
import braceexpand
from . import utils from . import utils
from .paddle_utils import IterableDataset from .paddle_utils import IterableDataset
from .utils import PipelineStage from .utils import PipelineStage
...@@ -63,8 +53,7 @@ class repeatedly(IterableDataset, PipelineStage): ...@@ -63,8 +53,7 @@ class repeatedly(IterableDataset, PipelineStage):
return utils.repeatedly( return utils.repeatedly(
source, source,
nepochs=self.nepochs, nepochs=self.nepochs,
nbatches=self.nbatches, nbatches=self.nbatches, )
)
class with_epoch(IterableDataset): class with_epoch(IterableDataset):
......
paddlespeech/audio/streamdata/filters.py
浏览文件 @ 43af22e4
...@@ -3,7 +3,6 @@ ...@@ -3,7 +3,6 @@
# This file is part of the WebDataset library. # This file is part of the WebDataset library.
# See the LICENSE file for licensing terms (BSD-style). # See the LICENSE file for licensing terms (BSD-style).
# #
# Modified from https://github.com/webdataset/webdataset # Modified from https://github.com/webdataset/webdataset
# Modified from wenet(https://github.com/wenet-e2e/wenet) # Modified from wenet(https://github.com/wenet-e2e/wenet)
"""A collection of iterators for data transformations. """A collection of iterators for data transformations.
...@@ -12,28 +11,29 @@ These functions are plain iterator functions. You can find curried versions ...@@ -12,28 +11,29 @@ These functions are plain iterator functions. You can find curried versions
in webdataset.filters, and you can find IterableDataset wrappers in in webdataset.filters, and you can find IterableDataset wrappers in
webdataset.processing. webdataset.processing.
""" """
import io import io
from fnmatch import fnmatch import itertools
import os
import random
import re import re
import itertools, os, random, sys, time import sys
from functools import reduce, wraps import time
from fnmatch import fnmatch
from functools import reduce
import numpy as np import paddle
from . import autodecode from . import autodecode
from . import utils from . import utils
from .paddle_utils import PaddleTensor
from .utils import PipelineStage
from .. import backends from .. import backends
from ..compliance import kaldi from ..compliance import kaldi
import paddle
from ..transform.cmvn import GlobalCMVN from ..transform.cmvn import GlobalCMVN
from ..utils.tensor_utils import pad_sequence
from ..transform.spec_augment import time_warp
from ..transform.spec_augment import time_mask
from ..transform.spec_augment import freq_mask from ..transform.spec_augment import freq_mask
from ..transform.spec_augment import time_mask
from ..transform.spec_augment import time_warp
from ..utils.tensor_utils import pad_sequence
from .utils import PipelineStage
class FilterFunction(object): class FilterFunction(object):
"""Helper class for currying pipeline stages. """Helper class for currying pipeline stages.
...@@ -159,10 +159,12 @@ def transform_with(sample, transformers): ...@@ -159,10 +159,12 @@ def transform_with(sample, transformers):
result[i] = f(sample[i]) result[i] = f(sample[i])
return result return result
### ###
# Iterators # Iterators
### ###
def _info(data, fmt=None, n=3, every=-1, width=50, stream=sys.stderr, name=""): def _info(data, fmt=None, n=3, every=-1, width=50, stream=sys.stderr, name=""):
"""Print information about the samples that are passing through. """Print information about the samples that are passing through.
...@@ -278,10 +280,16 @@ def _log_keys(data, logfile=None): ...@@ -278,10 +280,16 @@ def _log_keys(data, logfile=None):
log_keys = pipelinefilter(_log_keys) log_keys = pipelinefilter(_log_keys)
def _minedecode(x):
if isinstance(x, str):
return autodecode.imagehandler(x)
else:
return x
def _decode(data, *args, handler=reraise_exception, **kw): def _decode(data, *args, handler=reraise_exception, **kw):
"""Decode data based on the decoding functions given as arguments.""" """Decode data based on the decoding functions given as arguments."""
decoder = _minedecode
decoder = lambda x: autodecode.imagehandler(x) if isinstance(x, str) else x
handlers = [decoder(x) for x in args] handlers = [decoder(x) for x in args]
f = autodecode.Decoder(handlers, **kw) f = autodecode.Decoder(handlers, **kw)
...@@ -325,15 +333,24 @@ def _rename(data, handler=reraise_exception, keep=True, **kw): ...@@ -325,15 +333,24 @@ def _rename(data, handler=reraise_exception, keep=True, **kw):
for sample in data: for sample in data:
try: try:
if not keep: if not keep:
yield {k: getfirst(sample, v, missing_is_error=True) for k, v in kw.items()} yield {
k: getfirst(sample, v, missing_is_error=True)
for k, v in kw.items()
}
else: else:
def listify(v): def listify(v):
return v.split(";") if isinstance(v, str) else v return v.split(";") if isinstance(v, str) else v
to_be_replaced = {x for v in kw.values() for x in listify(v)} to_be_replaced = {x for v in kw.values() for x in listify(v)}
result = {k: v for k, v in sample.items() if k not in to_be_replaced} result = {
result.update({k: getfirst(sample, v, missing_is_error=True) for k, v in kw.items()}) k: v
for k, v in sample.items() if k not in to_be_replaced
}
result.update({
k: getfirst(sample, v, missing_is_error=True)
for k, v in kw.items()
})
yield result yield result
except Exception as exn: except Exception as exn:
if handler(exn): if handler(exn):
...@@ -381,7 +398,11 @@ def _map_dict(data, handler=reraise_exception, **kw): ...@@ -381,7 +398,11 @@ def _map_dict(data, handler=reraise_exception, **kw):
map_dict = pipelinefilter(_map_dict) map_dict = pipelinefilter(_map_dict)
def _to_tuple(data, *args, handler=reraise_exception, missing_is_error=True, none_is_error=None): def _to_tuple(data,
*args,
handler=reraise_exception,
missing_is_error=True,
none_is_error=None):
"""Convert dict samples to tuples.""" """Convert dict samples to tuples."""
if none_is_error is None: if none_is_error is None:
none_is_error = missing_is_error none_is_error = missing_is_error
...@@ -390,7 +411,10 @@ def _to_tuple(data, *args, handler=reraise_exception, missing_is_error=True, non ...@@ -390,7 +411,10 @@ def _to_tuple(data, *args, handler=reraise_exception, missing_is_error=True, non
for sample in data: for sample in data:
try: try:
result = tuple([getfirst(sample, f, missing_is_error=missing_is_error) for f in args]) result = tuple([
getfirst(sample, f, missing_is_error=missing_is_error)
for f in args
])
if none_is_error and any(x is None for x in result): if none_is_error and any(x is None for x in result):
raise ValueError(f"to_tuple {args} got {sample.keys()}") raise ValueError(f"to_tuple {args} got {sample.keys()}")
yield result yield result
...@@ -463,19 +487,28 @@ rsample = pipelinefilter(_rsample) ...@@ -463,19 +487,28 @@ rsample = pipelinefilter(_rsample)
slice = pipelinefilter(itertools.islice) slice = pipelinefilter(itertools.islice)
def _extract_keys(source, *patterns, duplicate_is_error=True, ignore_missing=False): def _extract_keys(source,
*patterns,
duplicate_is_error=True,
ignore_missing=False):
for sample in source: for sample in source:
result = [] result = []
for pattern in patterns: for pattern in patterns:
pattern = pattern.split(";") if isinstance(pattern, str) else pattern pattern = pattern.split(";") if isinstance(pattern,
matches = [x for x in sample.keys() if any(fnmatch("." + x, p) for p in pattern)] str) else pattern
matches = [
x for x in sample.keys()
if any(fnmatch("." + x, p) for p in pattern)
]
if len(matches) == 0: if len(matches) == 0:
if ignore_missing: if ignore_missing:
continue continue
else: else:
raise ValueError(f"Cannot find {pattern} in sample keys {sample.keys()}.") raise ValueError(
f"Cannot find {pattern} in sample keys {sample.keys()}.")
if len(matches) > 1 and duplicate_is_error: if len(matches) > 1 and duplicate_is_error:
raise ValueError(f"Multiple sample keys {sample.keys()} match {pattern}.") raise ValueError(
f"Multiple sample keys {sample.keys()} match {pattern}.")
value = sample[matches[0]] value = sample[matches[0]]
result.append(value) result.append(value)
yield tuple(result) yield tuple(result)
...@@ -484,7 +517,12 @@ def _extract_keys(source, *patterns, duplicate_is_error=True, ignore_missing=Fal ...@@ -484,7 +517,12 @@ def _extract_keys(source, *patterns, duplicate_is_error=True, ignore_missing=Fal
extract_keys = pipelinefilter(_extract_keys) extract_keys = pipelinefilter(_extract_keys)
def _rename_keys(source, *args, keep_unselected=False, must_match=True, duplicate_is_error=True, **kw): def _rename_keys(source,
*args,
keep_unselected=False,
must_match=True,
duplicate_is_error=True,
**kw):
renamings = [(pattern, output) for output, pattern in args] renamings = [(pattern, output) for output, pattern in args]
renamings += [(pattern, output) for output, pattern in kw.items()] renamings += [(pattern, output) for output, pattern in kw.items()]
for sample in source: for sample in source:
...@@ -504,11 +542,15 @@ def _rename_keys(source, *args, keep_unselected=False, must_match=True, duplicat ...@@ -504,11 +542,15 @@ def _rename_keys(source, *args, keep_unselected=False, must_match=True, duplicat
continue continue
if new_name in new_sample: if new_name in new_sample:
if duplicate_is_error: if duplicate_is_error:
raise ValueError(f"Duplicate value in sample {sample.keys()} after rename.") raise ValueError(
f"Duplicate value in sample {sample.keys()} after rename."
)
continue continue
new_sample[new_name] = value new_sample[new_name] = value
if must_match and not all(matched.values()): if must_match and not all(matched.values()):
raise ValueError(f"Not all patterns ({matched}) matched sample keys ({sample.keys()}).") raise ValueError(
f"Not all patterns ({matched}) matched sample keys ({sample.keys()})."
)
yield new_sample yield new_sample
...@@ -541,18 +583,18 @@ def find_decoder(decoders, path): ...@@ -541,18 +583,18 @@ def find_decoder(decoders, path):
if fname.startswith("__"): if fname.startswith("__"):
return lambda x: x return lambda x: x
for pattern, fun in decoders[::-1]: for pattern, fun in decoders[::-1]:
if fnmatch(fname.lower(), pattern) or fnmatch("." + fname.lower(), pattern): if fnmatch(fname.lower(), pattern) or fnmatch("." + fname.lower(),
pattern):
return fun return fun
return None return None
def _xdecode( def _xdecode(
source, source,
*args, *args,
must_decode=True, must_decode=True,
defaults=default_decoders, defaults=default_decoders,
**kw, **kw, ):
):
decoders = list(defaults) + list(args) decoders = list(defaults) + list(args)
decoders += [("*." + k, v) for k, v in kw.items()] decoders += [("*." + k, v) for k, v in kw.items()]
for sample in source: for sample in source:
...@@ -575,18 +617,18 @@ def _xdecode( ...@@ -575,18 +617,18 @@ def _xdecode(
new_sample[path] = value new_sample[path] = value
yield new_sample yield new_sample
xdecode = pipelinefilter(_xdecode)
xdecode = pipelinefilter(_xdecode)
def _audio_data_filter(source, def _audio_data_filter(source,
frame_shift=10, frame_shift=10,
max_length=10240, max_length=10240,
min_length=10, min_length=10,
token_max_length=200, token_max_length=200,
token_min_length=1, token_min_length=1,
min_output_input_ratio=0.0005, min_output_input_ratio=0.0005,
max_output_input_ratio=1): max_output_input_ratio=1):
""" Filter sample according to feature and label length """ Filter sample according to feature and label length
Inplace operation. Inplace operation.
...@@ -613,7 +655,8 @@ def _audio_data_filter(source, ...@@ -613,7 +655,8 @@ def _audio_data_filter(source,
assert 'wav' in sample assert 'wav' in sample
assert 'label' in sample assert 'label' in sample
# sample['wav'] is paddle.Tensor, we have 100 frames every second (default) # sample['wav'] is paddle.Tensor, we have 100 frames every second (default)
num_frames = sample['wav'].shape[1] / sample['sample_rate'] * (1000 / frame_shift) num_frames = sample['wav'].shape[1] / sample['sample_rate'] * (
1000 / frame_shift)
if num_frames < min_length: if num_frames < min_length:
continue continue
if num_frames > max_length: if num_frames > max_length:
...@@ -629,13 +672,15 @@ def _audio_data_filter(source, ...@@ -629,13 +672,15 @@ def _audio_data_filter(source,
continue continue
yield sample yield sample
audio_data_filter = pipelinefilter(_audio_data_filter) audio_data_filter = pipelinefilter(_audio_data_filter)
def _audio_tokenize(source, def _audio_tokenize(source,
symbol_table, symbol_table,
bpe_model=None, bpe_model=None,
non_lang_syms=None, non_lang_syms=None,
split_with_space=False): split_with_space=False):
""" Decode text to chars or BPE """ Decode text to chars or BPE
Inplace operation Inplace operation
...@@ -693,8 +738,10 @@ def _audio_tokenize(source, ...@@ -693,8 +738,10 @@ def _audio_tokenize(source,
sample['label'] = label sample['label'] = label
yield sample yield sample
audio_tokenize = pipelinefilter(_audio_tokenize) audio_tokenize = pipelinefilter(_audio_tokenize)
def _audio_resample(source, resample_rate=16000): def _audio_resample(source, resample_rate=16000):
""" Resample data. """ Resample data.
Inplace operation. Inplace operation.
...@@ -713,18 +760,22 @@ def _audio_resample(source, resample_rate=16000): ...@@ -713,18 +760,22 @@ def _audio_resample(source, resample_rate=16000):
waveform = sample['wav'] waveform = sample['wav']
if sample_rate != resample_rate: if sample_rate != resample_rate:
sample['sample_rate'] = resample_rate sample['sample_rate'] = resample_rate
sample['wav'] = paddle.to_tensor(backends.soundfile_backend.resample( sample['wav'] = paddle.to_tensor(
waveform.numpy(), src_sr = sample_rate, target_sr = resample_rate backends.soundfile_backend.resample(
)) waveform.numpy(),
src_sr=sample_rate,
target_sr=resample_rate))
yield sample yield sample
audio_resample = pipelinefilter(_audio_resample) audio_resample = pipelinefilter(_audio_resample)
def _audio_compute_fbank(source, def _audio_compute_fbank(source,
num_mel_bins=80, num_mel_bins=80,
frame_length=25, frame_length=25,
frame_shift=10, frame_shift=10,
dither=0.0): dither=0.0):
""" Extract fbank """ Extract fbank
Args: Args:
...@@ -746,30 +797,33 @@ def _audio_compute_fbank(source, ...@@ -746,30 +797,33 @@ def _audio_compute_fbank(source,
waveform = sample['wav'] waveform = sample['wav']
waveform = waveform * (1 << 15) waveform = waveform * (1 << 15)
# Only keep fname, feat, label # Only keep fname, feat, label
mat = kaldi.fbank(waveform, mat = kaldi.fbank(
n_mels=num_mel_bins, waveform,
frame_length=frame_length, n_mels=num_mel_bins,
frame_shift=frame_shift, frame_length=frame_length,
dither=dither, frame_shift=frame_shift,
energy_floor=0.0, dither=dither,
sr=sample_rate) energy_floor=0.0,
sr=sample_rate)
yield dict(fname=sample['fname'], label=sample['label'], feat=mat) yield dict(fname=sample['fname'], label=sample['label'], feat=mat)
audio_compute_fbank = pipelinefilter(_audio_compute_fbank) audio_compute_fbank = pipelinefilter(_audio_compute_fbank)
def _audio_spec_aug(source,
max_w=5, def _audio_spec_aug(
w_inplace=True, source,
w_mode="PIL", max_w=5,
max_f=30, w_inplace=True,
num_f_mask=2, w_mode="PIL",
f_inplace=True, max_f=30,
f_replace_with_zero=False, num_f_mask=2,
max_t=40, f_inplace=True,
num_t_mask=2, f_replace_with_zero=False,
t_inplace=True, max_t=40,
t_replace_with_zero=False,): num_t_mask=2,
t_inplace=True,
t_replace_with_zero=False, ):
""" Do spec augmentation """ Do spec augmentation
Inplace operation Inplace operation
...@@ -793,12 +847,23 @@ def _audio_spec_aug(source, ...@@ -793,12 +847,23 @@ def _audio_spec_aug(source,
for sample in source: for sample in source:
x = sample['feat'] x = sample['feat']
x = x.numpy() x = x.numpy()
x = time_warp(x, max_time_warp=max_w, inplace = w_inplace, mode= w_mode) x = time_warp(x, max_time_warp=max_w, inplace=w_inplace, mode=w_mode)
x = freq_mask(x, F = max_f, n_mask = num_f_mask, inplace = f_inplace, replace_with_zero = f_replace_with_zero) x = freq_mask(
x = time_mask(x, T = max_t, n_mask = num_t_mask, inplace = t_inplace, replace_with_zero = t_replace_with_zero) x,
F=max_f,
n_mask=num_f_mask,
inplace=f_inplace,
replace_with_zero=f_replace_with_zero)
x = time_mask(
x,
T=max_t,
n_mask=num_t_mask,
inplace=t_inplace,
replace_with_zero=t_replace_with_zero)
sample['feat'] = paddle.to_tensor(x, dtype=paddle.float32) sample['feat'] = paddle.to_tensor(x, dtype=paddle.float32)
yield sample yield sample
audio_spec_aug = pipelinefilter(_audio_spec_aug) audio_spec_aug = pipelinefilter(_audio_spec_aug)
...@@ -829,8 +894,10 @@ def _sort(source, sort_size=500): ...@@ -829,8 +894,10 @@ def _sort(source, sort_size=500):
for x in buf: for x in buf:
yield x yield x
sort = pipelinefilter(_sort) sort = pipelinefilter(_sort)
def _batched(source, batch_size=16): def _batched(source, batch_size=16):
""" Static batch the data by `batch_size` """ Static batch the data by `batch_size`
...@@ -850,8 +917,10 @@ def _batched(source, batch_size=16): ...@@ -850,8 +917,10 @@ def _batched(source, batch_size=16):
if len(buf) > 0: if len(buf) > 0:
yield buf yield buf
batched = pipelinefilter(_batched) batched = pipelinefilter(_batched)
def dynamic_batched(source, max_frames_in_batch=12000): def dynamic_batched(source, max_frames_in_batch=12000):
""" Dynamic batch the data until the total frames in batch """ Dynamic batch the data until the total frames in batch
reach `max_frames_in_batch` reach `max_frames_in_batch`
...@@ -892,8 +961,8 @@ def _audio_padding(source): ...@@ -892,8 +961,8 @@ def _audio_padding(source):
""" """
for sample in source: for sample in source:
assert isinstance(sample, list) assert isinstance(sample, list)
feats_length = paddle.to_tensor([x['feat'].shape[0] for x in sample], feats_length = paddle.to_tensor(
dtype="int64") [x['feat'].shape[0] for x in sample], dtype="int64")
order = paddle.argsort(feats_length, descending=True) order = paddle.argsort(feats_length, descending=True)
feats_lengths = paddle.to_tensor( feats_lengths = paddle.to_tensor(
[sample[i]['feat'].shape[0] for i in order], dtype="int64") [sample[i]['feat'].shape[0] for i in order], dtype="int64")
...@@ -902,20 +971,20 @@ def _audio_padding(source): ...@@ -902,20 +971,20 @@ def _audio_padding(source):
sorted_labels = [ sorted_labels = [
paddle.to_tensor(sample[i]['label'], dtype="int32") for i in order paddle.to_tensor(sample[i]['label'], dtype="int32") for i in order
] ]
label_lengths = paddle.to_tensor([x.shape[0] for x in sorted_labels], label_lengths = paddle.to_tensor(
dtype="int64") [x.shape[0] for x in sorted_labels], dtype="int64")
padded_feats = pad_sequence(sorted_feats, padded_feats = pad_sequence(
batch_first=True, sorted_feats, batch_first=True, padding_value=0)
padding_value=0) padding_labels = pad_sequence(
padding_labels = pad_sequence(sorted_labels, sorted_labels, batch_first=True, padding_value=-1)
batch_first=True,
padding_value=-1) yield (sorted_keys, padded_feats, feats_lengths, padding_labels,
yield (sorted_keys, padded_feats, feats_lengths, padding_labels,
label_lengths) label_lengths)
audio_padding = pipelinefilter(_audio_padding) audio_padding = pipelinefilter(_audio_padding)
def _audio_cmvn(source, cmvn_file): def _audio_cmvn(source, cmvn_file):
global_cmvn = GlobalCMVN(cmvn_file) global_cmvn = GlobalCMVN(cmvn_file)
for batch in source: for batch in source:
...@@ -923,13 +992,16 @@ def _audio_cmvn(source, cmvn_file): ...@@ -923,13 +992,16 @@ def _audio_cmvn(source, cmvn_file):
padded_feats = padded_feats.numpy() padded_feats = padded_feats.numpy()
padded_feats = global_cmvn(padded_feats) padded_feats = global_cmvn(padded_feats)
padded_feats = paddle.to_tensor(padded_feats, dtype=paddle.float32) padded_feats = paddle.to_tensor(padded_feats, dtype=paddle.float32)
yield (sorted_keys, padded_feats, feats_lengths, padding_labels, yield (sorted_keys, padded_feats, feats_lengths, padding_labels,
label_lengths) label_lengths)
audio_cmvn = pipelinefilter(_audio_cmvn) audio_cmvn = pipelinefilter(_audio_cmvn)
def _placeholder(source): def _placeholder(source):
for data in source: for data in source:
yield data yield data
placeholder = pipelinefilter(_placeholder) placeholder = pipelinefilter(_placeholder)
paddlespeech/audio/streamdata/gopen.py
浏览文件 @ 43af22e4
...@@ -3,12 +3,12 @@ ...@@ -3,12 +3,12 @@
# This file is part of the WebDataset library. # This file is part of the WebDataset library.
# See the LICENSE file for licensing terms (BSD-style). # See the LICENSE file for licensing terms (BSD-style).
# #
"""Open URLs by calling subcommands.""" """Open URLs by calling subcommands."""
import os
import os, sys, re import re
from subprocess import PIPE, Popen import sys
from subprocess import PIPE
from subprocess import Popen
from urllib.parse import urlparse from urllib.parse import urlparse
# global used for printing additional node information during verbose output # global used for printing additional node information during verbose output
...@@ -31,14 +31,13 @@ class Pipe: ...@@ -31,14 +31,13 @@ class Pipe:
""" """
def __init__( def __init__(
self, self,
*args, *args,
mode=None, mode=None,
timeout=7200.0, timeout=7200.0,
ignore_errors=False, ignore_errors=False,
ignore_status=[], ignore_status=[],
**kw, **kw, ):
):
"""Create an IO Pipe.""" """Create an IO Pipe."""
self.ignore_errors = ignore_errors self.ignore_errors = ignore_errors
self.ignore_status = [0] + ignore_status self.ignore_status = [0] + ignore_status
...@@ -75,8 +74,7 @@ class Pipe: ...@@ -75,8 +74,7 @@ class Pipe:
if verbose: if verbose:
print( print(
f"pipe exit [{self.status} {os.getpid()}:{self.proc.pid}] {self.args} {info}", f"pipe exit [{self.status} {os.getpid()}:{self.proc.pid}] {self.args} {info}",
file=sys.stderr, file=sys.stderr, )
)
if self.status not in self.ignore_status and not self.ignore_errors: if self.status not in self.ignore_status and not self.ignore_errors:
raise Exception(f"{self.args}: exit {self.status} (read) {info}") raise Exception(f"{self.args}: exit {self.status} (read) {info}")
...@@ -114,9 +112,11 @@ class Pipe: ...@@ -114,9 +112,11 @@ class Pipe:
self.close() self.close()
def set_options( def set_options(obj,
obj, timeout=None, ignore_errors=None, ignore_status=None, handler=None timeout=None,
): ignore_errors=None,
ignore_status=None,
handler=None):
"""Set options for Pipes. """Set options for Pipes.
This function can be called on any stream. It will set pipe options only This function can be called on any stream. It will set pipe options only
...@@ -168,16 +168,14 @@ def gopen_pipe(url, mode="rb", bufsize=8192): ...@@ -168,16 +168,14 @@ def gopen_pipe(url, mode="rb", bufsize=8192):
mode=mode, mode=mode,
shell=True, shell=True,
bufsize=bufsize, bufsize=bufsize,
ignore_status=[141], ignore_status=[141], ) # skipcq: BAN-B604
) # skipcq: BAN-B604
elif mode[0] == "w": elif mode[0] == "w":
return Pipe( return Pipe(
cmd, cmd,
mode=mode, mode=mode,
shell=True, shell=True,
bufsize=bufsize, bufsize=bufsize,
ignore_status=[141], ignore_status=[141], ) # skipcq: BAN-B604
) # skipcq: BAN-B604
else: else:
raise ValueError(f"{mode}: unknown mode") raise ValueError(f"{mode}: unknown mode")
...@@ -196,8 +194,7 @@ def gopen_curl(url, mode="rb", bufsize=8192): ...@@ -196,8 +194,7 @@ def gopen_curl(url, mode="rb", bufsize=8192):
mode=mode, mode=mode,
shell=True, shell=True,
bufsize=bufsize, bufsize=bufsize,
ignore_status=[141, 23], ignore_status=[141, 23], ) # skipcq: BAN-B604
) # skipcq: BAN-B604
elif mode[0] == "w": elif mode[0] == "w":
cmd = f"curl -s -L -T - '{url}'" cmd = f"curl -s -L -T - '{url}'"
return Pipe( return Pipe(
...@@ -205,8 +202,7 @@ def gopen_curl(url, mode="rb", bufsize=8192): ...@@ -205,8 +202,7 @@ def gopen_curl(url, mode="rb", bufsize=8192):
mode=mode, mode=mode,
shell=True, shell=True,
bufsize=bufsize, bufsize=bufsize,
ignore_status=[141, 26], ignore_status=[141, 26], ) # skipcq: BAN-B604
) # skipcq: BAN-B604
else: else:
raise ValueError(f"{mode}: unknown mode") raise ValueError(f"{mode}: unknown mode")
...@@ -226,15 +222,13 @@ def gopen_htgs(url, mode="rb", bufsize=8192): ...@@ -226,15 +222,13 @@ def gopen_htgs(url, mode="rb", bufsize=8192):
mode=mode, mode=mode,
shell=True, shell=True,
bufsize=bufsize, bufsize=bufsize,
ignore_status=[141, 23], ignore_status=[141, 23], ) # skipcq: BAN-B604
) # skipcq: BAN-B604
elif mode[0] == "w": elif mode[0] == "w":
raise ValueError(f"{mode}: cannot write") raise ValueError(f"{mode}: cannot write")
else: else:
raise ValueError(f"{mode}: unknown mode") raise ValueError(f"{mode}: unknown mode")
def gopen_gsutil(url, mode="rb", bufsize=8192): def gopen_gsutil(url, mode="rb", bufsize=8192):
"""Open a URL with `curl`. """Open a URL with `curl`.
...@@ -249,8 +243,7 @@ def gopen_gsutil(url, mode="rb", bufsize=8192): ...@@ -249,8 +243,7 @@ def gopen_gsutil(url, mode="rb", bufsize=8192):
mode=mode, mode=mode,
shell=True, shell=True,
bufsize=bufsize, bufsize=bufsize,
ignore_status=[141, 23], ignore_status=[141, 23], ) # skipcq: BAN-B604
) # skipcq: BAN-B604
elif mode[0] == "w": elif mode[0] == "w":
cmd = f"gsutil cp - '{url}'" cmd = f"gsutil cp - '{url}'"
return Pipe( return Pipe(
...@@ -258,13 +251,11 @@ def gopen_gsutil(url, mode="rb", bufsize=8192): ...@@ -258,13 +251,11 @@ def gopen_gsutil(url, mode="rb", bufsize=8192):
mode=mode, mode=mode,
shell=True, shell=True,
bufsize=bufsize, bufsize=bufsize,
ignore_status=[141, 26], ignore_status=[141, 26], ) # skipcq: BAN-B604
) # skipcq: BAN-B604
else: else:
raise ValueError(f"{mode}: unknown mode") raise ValueError(f"{mode}: unknown mode")
def gopen_error(url, *args, **kw): def gopen_error(url, *args, **kw):
"""Raise a value error. """Raise a value error.
...@@ -285,8 +276,7 @@ gopen_schemes = dict( ...@@ -285,8 +276,7 @@ gopen_schemes = dict(
ftps=gopen_curl, ftps=gopen_curl,
scp=gopen_curl, scp=gopen_curl,
gs=gopen_gsutil, gs=gopen_gsutil,
htgs=gopen_htgs, htgs=gopen_htgs, )
)
def gopen(url, mode="rb", bufsize=8192, **kw): def gopen(url, mode="rb", bufsize=8192, **kw):
......
paddlespeech/audio/streamdata/handlers.py
浏览文件 @ 43af22e4
...@@ -3,7 +3,6 @@ ...@@ -3,7 +3,6 @@
# This file is part of the WebDataset library. # This file is part of the WebDataset library.
# See the LICENSE file for licensing terms (BSD-style). # See the LICENSE file for licensing terms (BSD-style).
# #
"""Pluggable exception handlers. """Pluggable exception handlers.
These are functions that take an exception as an argument and then return... These are functions that take an exception as an argument and then return...
...@@ -14,8 +13,8 @@ These are functions that take an exception as an argument and then return... ...@@ -14,8 +13,8 @@ These are functions that take an exception as an argument and then return...
They are used as handler= arguments in much of the library. They are used as handler= arguments in much of the library.
""" """
import time
import time, warnings import warnings
def reraise_exception(exn): def reraise_exception(exn):
......
paddlespeech/audio/streamdata/mix.py
浏览文件 @ 43af22e4
...@@ -5,17 +5,12 @@ ...@@ -5,17 +5,12 @@
# See the LICENSE file for licensing terms (BSD-style). # See the LICENSE file for licensing terms (BSD-style).
# Modified from https://github.com/webdataset/webdataset # Modified from https://github.com/webdataset/webdataset
# #
"""Classes for mixing samples from multiple sources.""" """Classes for mixing samples from multiple sources."""
import random
import itertools, os, random, time, sys
from functools import reduce, wraps
import numpy as np import numpy as np
from . import autodecode, utils from .paddle_utils import IterableDataset
from .paddle_utils import PaddleTensor, IterableDataset
from .utils import PipelineStage
def round_robin_shortest(*sources): def round_robin_shortest(*sources):
......
paddlespeech/audio/streamdata/paddle_utils.py
浏览文件 @ 43af22e4
...@@ -5,12 +5,11 @@ ...@@ -5,12 +5,11 @@
# See the LICENSE file for licensing terms (BSD-style). # See the LICENSE file for licensing terms (BSD-style).
# Modified from https://github.com/webdataset/webdataset # Modified from https://github.com/webdataset/webdataset
# #
"""Mock implementations of paddle interfaces when paddle is not available.""" """Mock implementations of paddle interfaces when paddle is not available."""
try: try:
from paddle.io import DataLoader, IterableDataset from paddle.io import DataLoader
from paddle.io import IterableDataset
except ModuleNotFoundError: except ModuleNotFoundError:
class IterableDataset: class IterableDataset:
...@@ -22,12 +21,3 @@ except ModuleNotFoundError: ...@@ -22,12 +21,3 @@ except ModuleNotFoundError:
"""Empty implementation of DataLoader when paddle is not available.""" """Empty implementation of DataLoader when paddle is not available."""
pass pass
try:
from paddle import Tensor as PaddleTensor
except ModuleNotFoundError:
class TorchTensor:
"""Empty implementation of PaddleTensor when paddle is not available."""
pass
paddlespeech/audio/streamdata/pipeline.py
浏览文件 @ 43af22e4
...@@ -3,15 +3,12 @@ ...@@ -3,15 +3,12 @@
# See the LICENSE file for licensing terms (BSD-style). # See the LICENSE file for licensing terms (BSD-style).
# Modified from https://github.com/webdataset/webdataset # Modified from https://github.com/webdataset/webdataset
#%% #%%
import copy, os, random, sys, time import copy
from dataclasses import dataclass import sys
from itertools import islice from itertools import islice
from typing import List
import braceexpand, yaml from .paddle_utils import DataLoader
from .paddle_utils import IterableDataset
from .handlers import reraise_exception
from .paddle_utils import DataLoader, IterableDataset
from .utils import PipelineStage from .utils import PipelineStage
...@@ -22,8 +19,7 @@ def add_length_method(obj): ...@@ -22,8 +19,7 @@ def add_length_method(obj):
Combined = type( Combined = type(
obj.__class__.__name__ + "_Length", obj.__class__.__name__ + "_Length",
(obj.__class__, IterableDataset), (obj.__class__, IterableDataset),
{"__len__": length}, {"__len__": length}, )
)
obj.__class__ = Combined obj.__class__ = Combined
return obj return obj
......
paddlespeech/audio/streamdata/shardlists.py
浏览文件 @ 43af22e4
...@@ -4,28 +4,30 @@ ...@@ -4,28 +4,30 @@
# This file is part of the WebDataset library. # This file is part of the WebDataset library.
# See the LICENSE file for licensing terms (BSD-style). # See the LICENSE file for licensing terms (BSD-style).
# #
# Modified from https://github.com/webdataset/webdataset # Modified from https://github.com/webdataset/webdataset
"""Train PyTorch models directly from POSIX tar archive. """Train PyTorch models directly from POSIX tar archive.
Code works locally or over HTTP connections. Code works locally or over HTTP connections.
""" """
import os
import os, random, sys, time import random
from dataclasses import dataclass, field import sys
import time
from dataclasses import dataclass
from dataclasses import field
from itertools import islice from itertools import islice
from typing import List from typing import List
import braceexpand, yaml import braceexpand
import yaml
from . import utils from . import utils
from ..utils.log import Logger
from .filters import pipelinefilter from .filters import pipelinefilter
from .paddle_utils import IterableDataset from .paddle_utils import IterableDataset
logger = Logger(__name__)
from ..utils.log import Logger
logger = Logger(__name__)
def expand_urls(urls): def expand_urls(urls):
if isinstance(urls, str): if isinstance(urls, str):
urllist = urls.split("::") urllist = urls.split("::")
...@@ -64,7 +66,8 @@ class SimpleShardList(IterableDataset): ...@@ -64,7 +66,8 @@ class SimpleShardList(IterableDataset):
def split_by_node(src, group=None): def split_by_node(src, group=None):
rank, world_size, worker, num_workers = utils.paddle_worker_info(group=group) rank, world_size, worker, num_workers = utils.paddle_worker_info(
group=group)
logger.info(f"world_size:{world_size}, rank:{rank}") logger.info(f"world_size:{world_size}, rank:{rank}")
if world_size > 1: if world_size > 1:
for s in islice(src, rank, None, world_size): for s in islice(src, rank, None, world_size):
...@@ -75,9 +78,11 @@ def split_by_node(src, group=None): ...@@ -75,9 +78,11 @@ def split_by_node(src, group=None):
def single_node_only(src, group=None): def single_node_only(src, group=None):
rank, world_size, worker, num_workers = utils.paddle_worker_info(group=group) rank, world_size, worker, num_workers = utils.paddle_worker_info(
group=group)
if world_size > 1: if world_size > 1:
raise ValueError("input pipeline needs to be reconfigured for multinode training") raise ValueError(
"input pipeline needs to be reconfigured for multinode training")
for s in src: for s in src:
yield s yield s
...@@ -104,7 +109,8 @@ def resampled_(src, n=sys.maxsize): ...@@ -104,7 +109,8 @@ def resampled_(src, n=sys.maxsize):
rng = random.Random(seed) rng = random.Random(seed)
print("# resampled loading", file=sys.stderr) print("# resampled loading", file=sys.stderr)
items = list(src) items = list(src)
print(f"# resampled got {len(items)} samples, yielding {n}", file=sys.stderr) print(
f"# resampled got {len(items)} samples, yielding {n}", file=sys.stderr)
for i in range(n): for i in range(n):
yield rng.choice(items) yield rng.choice(items)
...@@ -118,7 +124,9 @@ def non_empty(src): ...@@ -118,7 +124,9 @@ def non_empty(src):
yield s yield s
count += 1 count += 1
if count == 0: if count == 0:
raise ValueError("pipeline stage received no data at all and this was declared as an error") raise ValueError(
"pipeline stage received no data at all and this was declared as an error"
)
@dataclass @dataclass
...@@ -138,10 +146,6 @@ def expand(s): ...@@ -138,10 +146,6 @@ def expand(s):
return os.path.expanduser(os.path.expandvars(s)) return os.path.expanduser(os.path.expandvars(s))
class MultiShardSample(IterableDataset):
def __init__(self, fname):
"""Construct a shardlist from multiple sources using a YAML spec."""
self.epoch = -1
class MultiShardSample(IterableDataset): class MultiShardSample(IterableDataset):
def __init__(self, fname): def __init__(self, fname):
"""Construct a shardlist from multiple sources using a YAML spec.""" """Construct a shardlist from multiple sources using a YAML spec."""
...@@ -156,20 +160,23 @@ class MultiShardSample(IterableDataset): ...@@ -156,20 +160,23 @@ class MultiShardSample(IterableDataset):
else: else:
with open(fname) as stream: with open(fname) as stream:
spec = yaml.safe_load(stream) spec = yaml.safe_load(stream)
assert set(spec.keys()).issubset(set("prefix datasets buckets".split())), list(spec.keys()) assert set(spec.keys()).issubset(
set("prefix datasets buckets".split())), list(spec.keys())
prefix = expand(spec.get("prefix", "")) prefix = expand(spec.get("prefix", ""))
self.sources = [] self.sources = []
for ds in spec["datasets"]: for ds in spec["datasets"]:
assert set(ds.keys()).issubset(set("buckets name shards resample choose".split())), list( assert set(ds.keys()).issubset(
ds.keys() set("buckets name shards resample choose".split())), list(
) ds.keys())
buckets = ds.get("buckets", spec.get("buckets", [])) buckets = ds.get("buckets", spec.get("buckets", []))
if isinstance(buckets, str): if isinstance(buckets, str):
buckets = [buckets] buckets = [buckets]
buckets = [expand(s) for s in buckets] buckets = [expand(s) for s in buckets]
if buckets == []: if buckets == []:
buckets = [""] buckets = [""]
assert len(buckets) == 1, f"{buckets}: FIXME support for multiple buckets unimplemented" assert len(
buckets
) == 1, f"{buckets}: FIXME support for multiple buckets unimplemented"
bucket = buckets[0] bucket = buckets[0]
name = ds.get("name", "@" + bucket) name = ds.get("name", "@" + bucket)
urls = ds["shards"] urls = ds["shards"]
...@@ -177,15 +184,19 @@ class MultiShardSample(IterableDataset): ...@@ -177,15 +184,19 @@ class MultiShardSample(IterableDataset):
urls = [urls] urls = [urls]
# urls = [u for url in urls for u in braceexpand.braceexpand(url)] # urls = [u for url in urls for u in braceexpand.braceexpand(url)]
urls = [ urls = [
prefix + os.path.join(bucket, u) for url in urls for u in braceexpand.braceexpand(expand(url)) prefix + os.path.join(bucket, u)
for url in urls for u in braceexpand.braceexpand(expand(url))
] ]
resample = ds.get("resample", -1) resample = ds.get("resample", -1)
nsample = ds.get("choose", -1) nsample = ds.get("choose", -1)
if nsample > len(urls): if nsample > len(urls):
raise ValueError(f"perepoch {nsample} must be no greater than the number of shards") raise ValueError(
f"perepoch {nsample} must be no greater than the number of shards"
)
if (nsample > 0) and (resample > 0): if (nsample > 0) and (resample > 0):
raise ValueError("specify only one of perepoch or choose") raise ValueError("specify only one of perepoch or choose")
entry = MSSource(name=name, urls=urls, perepoch=nsample, resample=resample) entry = MSSource(
name=name, urls=urls, perepoch=nsample, resample=resample)
self.sources.append(entry) self.sources.append(entry)
print(f"# {name} {len(urls)} {nsample}", file=sys.stderr) print(f"# {name} {len(urls)} {nsample}", file=sys.stderr)
...@@ -203,7 +214,7 @@ class MultiShardSample(IterableDataset): ...@@ -203,7 +214,7 @@ class MultiShardSample(IterableDataset):
# sample without replacement # sample without replacement
l = list(source.urls) l = list(source.urls)
self.rng.shuffle(l) self.rng.shuffle(l)
l = l[: source.perepoch] l = l[:source.perepoch]
else: else:
l = list(source.urls) l = list(source.urls)
result += l result += l
...@@ -227,12 +238,11 @@ class ResampledShards(IterableDataset): ...@@ -227,12 +238,11 @@ class ResampledShards(IterableDataset):
"""An iterable dataset yielding a list of urls.""" """An iterable dataset yielding a list of urls."""
def __init__( def __init__(
self, self,
urls, urls,
nshards=sys.maxsize, nshards=sys.maxsize,
worker_seed=None, worker_seed=None,
deterministic=False, deterministic=False, ):
):
"""Sample shards from the shard list with replacement. """Sample shards from the shard list with replacement.
:param urls: a list of URLs as a Python list or brace notation string :param urls: a list of URLs as a Python list or brace notation string
...@@ -252,7 +262,8 @@ class ResampledShards(IterableDataset): ...@@ -252,7 +262,8 @@ class ResampledShards(IterableDataset):
if self.deterministic: if self.deterministic:
seed = utils.make_seed(self.worker_seed(), self.epoch) seed = utils.make_seed(self.worker_seed(), self.epoch)
else: else:
seed = utils.make_seed(self.worker_seed(), self.epoch, os.getpid(), time.time_ns(), os.urandom(4)) seed = utils.make_seed(self.worker_seed(), self.epoch,
os.getpid(), time.time_ns(), os.urandom(4))
if os.environ.get("WDS_SHOW_SEED", "0") == "1": if os.environ.get("WDS_SHOW_SEED", "0") == "1":
print(f"# ResampledShards seed {seed}") print(f"# ResampledShards seed {seed}")
self.rng = random.Random(seed) self.rng = random.Random(seed)
......
paddlespeech/audio/streamdata/tariterators.py
浏览文件 @ 43af22e4
...@@ -3,13 +3,12 @@ ...@@ -3,13 +3,12 @@
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# This file is part of the WebDataset library. # This file is part of the WebDataset library.
# See the LICENSE file for licensing terms (BSD-style). # See the LICENSE file for licensing terms (BSD-style).
# Modified from https://github.com/webdataset/webdataset # Modified from https://github.com/webdataset/webdataset
# Modified from wenet(https://github.com/wenet-e2e/wenet) # Modified from wenet(https://github.com/wenet-e2e/wenet)
"""Low level iteration functions for tar archives.""" """Low level iteration functions for tar archives."""
import random
import random, re, tarfile import re
import tarfile
import braceexpand import braceexpand
...@@ -27,6 +26,7 @@ import numpy as np ...@@ -27,6 +26,7 @@ import numpy as np
AUDIO_FORMAT_SETS = set(['flac', 'mp3', 'm4a', 'ogg', 'opus', 'wav', 'wma']) AUDIO_FORMAT_SETS = set(['flac', 'mp3', 'm4a', 'ogg', 'opus', 'wav', 'wma'])
def base_plus_ext(path): def base_plus_ext(path):
"""Split off all file extensions. """Split off all file extensions.
...@@ -47,12 +47,8 @@ def valid_sample(sample): ...@@ -47,12 +47,8 @@ def valid_sample(sample):
:param sample: sample to be checked :param sample: sample to be checked
""" """
return ( return (sample is not None and isinstance(sample, dict) and
sample is not None len(list(sample.keys())) > 0 and not sample.get("__bad__", False))
and isinstance(sample, dict)
and len(list(sample.keys())) > 0
and not sample.get("__bad__", False)
)
# FIXME: UNUSED # FIXME: UNUSED
...@@ -79,16 +75,16 @@ def url_opener(data, handler=reraise_exception, **kw): ...@@ -79,16 +75,16 @@ def url_opener(data, handler=reraise_exception, **kw):
sample.update(stream=stream) sample.update(stream=stream)
yield sample yield sample
except Exception as exn: except Exception as exn:
exn.args = exn.args + (url,) exn.args = exn.args + (url, )
if handler(exn): if handler(exn):
continue continue
else: else:
break break
def tar_file_iterator( def tar_file_iterator(fileobj,
fileobj, skip_meta=r"__[^/]*__($|/)", handler=reraise_exception skip_meta=r"__[^/]*__($|/)",
): handler=reraise_exception):
"""Iterate over tar file, yielding filename, content pairs for the given tar stream. """Iterate over tar file, yielding filename, content pairs for the given tar stream.
:param fileobj: byte stream suitable for tarfile :param fileobj: byte stream suitable for tarfile
...@@ -103,11 +99,8 @@ def tar_file_iterator( ...@@ -103,11 +99,8 @@ def tar_file_iterator(
continue continue
if fname is None: if fname is None:
continue continue
if ( if ("/" not in fname and fname.startswith(meta_prefix) and
"/" not in fname fname.endswith(meta_suffix)):
and fname.startswith(meta_prefix)
and fname.endswith(meta_suffix)
):
# skipping metadata for now # skipping metadata for now
continue continue
if skip_meta is not None and re.match(skip_meta, fname): if skip_meta is not None and re.match(skip_meta, fname):
...@@ -118,8 +111,10 @@ def tar_file_iterator( ...@@ -118,8 +111,10 @@ def tar_file_iterator(
assert pos > 0 assert pos > 0
prefix, postfix = name[:pos], name[pos + 1:] prefix, postfix = name[:pos], name[pos + 1:]
if postfix == 'wav': if postfix == 'wav':
waveform, sample_rate = paddlespeech.audio.load(stream.extractfile(tarinfo), normal=False) waveform, sample_rate = paddlespeech.audio.load(
result = dict(fname=prefix, wav=waveform, sample_rate = sample_rate) stream.extractfile(tarinfo), normal=False)
result = dict(
fname=prefix, wav=waveform, sample_rate=sample_rate)
else: else:
txt = stream.extractfile(tarinfo).read().decode('utf8').strip() txt = stream.extractfile(tarinfo).read().decode('utf8').strip()
result = dict(fname=prefix, txt=txt) result = dict(fname=prefix, txt=txt)
...@@ -128,16 +123,17 @@ def tar_file_iterator( ...@@ -128,16 +123,17 @@ def tar_file_iterator(
stream.members = [] stream.members = []
except Exception as exn: except Exception as exn:
if hasattr(exn, "args") and len(exn.args) > 0: if hasattr(exn, "args") and len(exn.args) > 0:
exn.args = (exn.args[0] + " @ " + str(fileobj),) + exn.args[1:] exn.args = (exn.args[0] + " @ " + str(fileobj), ) + exn.args[1:]
if handler(exn): if handler(exn):
continue continue
else: else:
break break
del stream del stream
def tar_file_and_group_iterator(
fileobj, skip_meta=r"__[^/]*__($|/)", handler=reraise_exception def tar_file_and_group_iterator(fileobj,
): skip_meta=r"__[^/]*__($|/)",
handler=reraise_exception):
""" Expand a stream of open tar files into a stream of tar file contents. """ Expand a stream of open tar files into a stream of tar file contents.
And groups the file with same prefix And groups the file with same prefix
...@@ -167,8 +163,11 @@ def tar_file_and_group_iterator( ...@@ -167,8 +163,11 @@ def tar_file_and_group_iterator(
if postfix == 'txt': if postfix == 'txt':
example['txt'] = file_obj.read().decode('utf8').strip() example['txt'] = file_obj.read().decode('utf8').strip()
elif postfix in AUDIO_FORMAT_SETS: elif postfix in AUDIO_FORMAT_SETS:
waveform, sample_rate = paddlespeech.audio.load(file_obj, normal=False) waveform, sample_rate = paddlespeech.audio.load(
waveform = paddle.to_tensor(np.expand_dims(np.array(waveform),0), dtype=paddle.float32) file_obj, normal=False)
waveform = paddle.to_tensor(
np.expand_dims(np.array(waveform), 0),
dtype=paddle.float32)
example['wav'] = waveform example['wav'] = waveform
example['sample_rate'] = sample_rate example['sample_rate'] = sample_rate
...@@ -176,19 +175,21 @@ def tar_file_and_group_iterator( ...@@ -176,19 +175,21 @@ def tar_file_and_group_iterator(
example[postfix] = file_obj.read() example[postfix] = file_obj.read()
except Exception as exn: except Exception as exn:
if hasattr(exn, "args") and len(exn.args) > 0: if hasattr(exn, "args") and len(exn.args) > 0:
exn.args = (exn.args[0] + " @ " + str(fileobj),) + exn.args[1:] exn.args = (exn.args[0] + " @ " + str(fileobj),
) + exn.args[1:]
if handler(exn): if handler(exn):
continue continue
else: else:
break break
valid = False valid = False
# logging.warning('error to parse {}'.format(name)) # logging.warning('error to parse {}'.format(name))
prev_prefix = prefix prev_prefix = prefix
if prev_prefix is not None: if prev_prefix is not None:
example['fname'] = prev_prefix example['fname'] = prev_prefix
yield example yield example
stream.close() stream.close()
def tar_file_expander(data, handler=reraise_exception): def tar_file_expander(data, handler=reraise_exception):
"""Expand a stream of open tar files into a stream of tar file contents. """Expand a stream of open tar files into a stream of tar file contents.
...@@ -200,9 +201,8 @@ def tar_file_expander(data, handler=reraise_exception): ...@@ -200,9 +201,8 @@ def tar_file_expander(data, handler=reraise_exception):
assert isinstance(source, dict) assert isinstance(source, dict)
assert "stream" in source assert "stream" in source
for sample in tar_file_iterator(source["stream"]): for sample in tar_file_iterator(source["stream"]):
assert ( assert (isinstance(sample, dict) and "data" in sample and
isinstance(sample, dict) and "data" in sample and "fname" in sample "fname" in sample)
)
sample["__url__"] = url sample["__url__"] = url
yield sample yield sample
except Exception as exn: except Exception as exn:
...@@ -213,8 +213,6 @@ def tar_file_expander(data, handler=reraise_exception): ...@@ -213,8 +213,6 @@ def tar_file_expander(data, handler=reraise_exception):
break break
def tar_file_and_group_expander(data, handler=reraise_exception): def tar_file_and_group_expander(data, handler=reraise_exception):
"""Expand a stream of open tar files into a stream of tar file contents. """Expand a stream of open tar files into a stream of tar file contents.
...@@ -226,9 +224,8 @@ def tar_file_and_group_expander(data, handler=reraise_exception): ...@@ -226,9 +224,8 @@ def tar_file_and_group_expander(data, handler=reraise_exception):
assert isinstance(source, dict) assert isinstance(source, dict)
assert "stream" in source assert "stream" in source
for sample in tar_file_and_group_iterator(source["stream"]): for sample in tar_file_and_group_iterator(source["stream"]):
assert ( assert (isinstance(sample, dict) and "wav" in sample and
isinstance(sample, dict) and "wav" in sample and "txt" in sample and "fname" in sample "txt" in sample and "fname" in sample)
)
sample["__url__"] = url sample["__url__"] = url
yield sample yield sample
except Exception as exn: except Exception as exn:
...@@ -239,7 +236,11 @@ def tar_file_and_group_expander(data, handler=reraise_exception): ...@@ -239,7 +236,11 @@ def tar_file_and_group_expander(data, handler=reraise_exception):
break break
def group_by_keys(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None): def group_by_keys(data,
keys=base_plus_ext,
lcase=True,
suffixes=None,
handler=None):
"""Return function over iterator that groups key, value pairs into samples. """Return function over iterator that groups key, value pairs into samples.
:param keys: function that splits the key into key and extension (base_plus_ext) :param keys: function that splits the key into key and extension (base_plus_ext)
...@@ -254,8 +255,8 @@ def group_by_keys(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=N ...@@ -254,8 +255,8 @@ def group_by_keys(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=N
print( print(
prefix, prefix,
suffix, suffix,
current_sample.keys() if isinstance(current_sample, dict) else None, current_sample.keys()
) if isinstance(current_sample, dict) else None, )
if prefix is None: if prefix is None:
continue continue
if lcase: if lcase:
......
paddlespeech/audio/streamdata/utils.py
浏览文件 @ 43af22e4
...@@ -4,22 +4,23 @@ ...@@ -4,22 +4,23 @@
# This file is part of the WebDataset library. # This file is part of the WebDataset library.
# See the LICENSE file for licensing terms (BSD-style). # See the LICENSE file for licensing terms (BSD-style).
# #
# Modified from https://github.com/webdataset/webdataset # Modified from https://github.com/webdataset/webdataset
"""Miscellaneous utility functions.""" """Miscellaneous utility functions."""
import importlib import importlib
import itertools as itt import itertools as itt
import os import os
import re import re
import sys import sys
from typing import Any, Callable, Iterator, Optional, Union from typing import Any
from typing import Callable
from typing import Iterator
from typing import Union
from ..utils.log import Logger from ..utils.log import Logger
logger = Logger(__name__) logger = Logger(__name__)
def make_seed(*args): def make_seed(*args):
seed = 0 seed = 0
for arg in args: for arg in args:
...@@ -37,7 +38,7 @@ def identity(x: Any) -> Any: ...@@ -37,7 +38,7 @@ def identity(x: Any) -> Any:
return x return x
def safe_eval(s: str, expr: str = "{}"): def safe_eval(s: str, expr: str="{}"):
"""Evaluate the given expression more safely.""" """Evaluate the given expression more safely."""
if re.sub("[^A-Za-z0-9_]", "", s) != s: if re.sub("[^A-Za-z0-9_]", "", s) != s:
raise ValueError(f"safe_eval: illegal characters in: '{s}'") raise ValueError(f"safe_eval: illegal characters in: '{s}'")
...@@ -54,9 +55,9 @@ def lookup_sym(sym: str, modules: list): ...@@ -54,9 +55,9 @@ def lookup_sym(sym: str, modules: list):
return None return None
def repeatedly0( def repeatedly0(loader: Iterator,
loader: Iterator, nepochs: int = sys.maxsize, nbatches: int = sys.maxsize nepochs: int=sys.maxsize,
): nbatches: int=sys.maxsize):
"""Repeatedly returns batches from a DataLoader.""" """Repeatedly returns batches from a DataLoader."""
for epoch in range(nepochs): for epoch in range(nepochs):
for sample in itt.islice(loader, nbatches): for sample in itt.islice(loader, nbatches):
...@@ -69,12 +70,11 @@ def guess_batchsize(batch: Union[tuple, list]): ...@@ -69,12 +70,11 @@ def guess_batchsize(batch: Union[tuple, list]):
def repeatedly( def repeatedly(
source: Iterator, source: Iterator,
nepochs: int = None, nepochs: int=None,
nbatches: int = None, nbatches: int=None,
nsamples: int = None, nsamples: int=None,
batchsize: Callable[..., int] = guess_batchsize, batchsize: Callable[..., int]=guess_batchsize, ):
):
"""Repeatedly yield samples from an iterator.""" """Repeatedly yield samples from an iterator."""
epoch = 0 epoch = 0
batch = 0 batch = 0
...@@ -93,6 +93,7 @@ def repeatedly( ...@@ -93,6 +93,7 @@ def repeatedly(
if nepochs is not None and epoch >= nepochs: if nepochs is not None and epoch >= nepochs:
return return
def paddle_worker_info(group=None): def paddle_worker_info(group=None):
"""Return node and worker info for PyTorch and some distributed environments.""" """Return node and worker info for PyTorch and some distributed environments."""
rank = 0 rank = 0
...@@ -116,7 +117,7 @@ def paddle_worker_info(group=None): ...@@ -116,7 +117,7 @@ def paddle_worker_info(group=None):
else: else:
try: try:
from paddle.io import get_worker_info from paddle.io import get_worker_info
worker_info = paddle.io.get_worker_info() worker_info = get_worker_info()
if worker_info is not None: if worker_info is not None:
worker = worker_info.id worker = worker_info.id
num_workers = worker_info.num_workers num_workers = worker_info.num_workers
...@@ -126,6 +127,7 @@ def paddle_worker_info(group=None): ...@@ -126,6 +127,7 @@ def paddle_worker_info(group=None):
return rank, world_size, worker, num_workers return rank, world_size, worker, num_workers
def paddle_worker_seed(group=None): def paddle_worker_seed(group=None):
"""Compute a distinct, deterministic RNG seed for each worker and node.""" """Compute a distinct, deterministic RNG seed for each worker and node."""
rank, world_size, worker, num_workers = paddle_worker_info(group=group) rank, world_size, worker, num_workers = paddle_worker_info(group=group)
......
paddlespeech/audio/streamdata/writer.py
浏览文件 @ 43af22e4
...@@ -5,18 +5,24 @@ ...@@ -5,18 +5,24 @@
# See the LICENSE file for licensing terms (BSD-style). # See the LICENSE file for licensing terms (BSD-style).
# Modified from https://github.com/webdataset/webdataset # Modified from https://github.com/webdataset/webdataset
# #
"""Classes and functions for writing tar files and WebDataset files.""" """Classes and functions for writing tar files and WebDataset files."""
import io
import io, json, pickle, re, tarfile, time import json
from typing import Any, Callable, Optional, Union import pickle
import re
import tarfile
import time
from typing import Any
from typing import Callable
from typing import Optional
from typing import Union
import numpy as np import numpy as np
from . import gopen from . import gopen
def imageencoder(image: Any, format: str = "PNG"): # skipcq: PYL-W0622 def imageencoder(image: Any, format: str="PNG"): # skipcq: PYL-W0622
"""Compress an image using PIL and return it as a string. """Compress an image using PIL and return it as a string.
Can handle float or uint8 images. Can handle float or uint8 images.
...@@ -67,6 +73,7 @@ def bytestr(data: Any): ...@@ -67,6 +73,7 @@ def bytestr(data: Any):
return data.encode("ascii") return data.encode("ascii")
return str(data).encode("ascii") return str(data).encode("ascii")
def paddle_dumps(data: Any): def paddle_dumps(data: Any):
"""Dump data into a bytestring using paddle.dumps. """Dump data into a bytestring using paddle.dumps.
...@@ -82,6 +89,7 @@ def paddle_dumps(data: Any): ...@@ -82,6 +89,7 @@ def paddle_dumps(data: Any):
paddle.save(data, stream) paddle.save(data, stream)
return stream.getvalue() return stream.getvalue()
def numpy_dumps(data: np.ndarray): def numpy_dumps(data: np.ndarray):
"""Dump data into a bytestring using numpy npy format. """Dump data into a bytestring using numpy npy format.
...@@ -139,9 +147,8 @@ def add_handlers(d, keys, value): ...@@ -139,9 +147,8 @@ def add_handlers(d, keys, value):
def make_handlers(): def make_handlers():
"""Create a list of handlers for encoding data.""" """Create a list of handlers for encoding data."""
handlers = {} handlers = {}
add_handlers( add_handlers(handlers, "cls cls2 class count index inx id",
handlers, "cls cls2 class count index inx id", lambda x: str(x).encode("ascii") lambda x: str(x).encode("ascii"))
)
add_handlers(handlers, "txt text transcript", lambda x: x.encode("utf-8")) add_handlers(handlers, "txt text transcript", lambda x: x.encode("utf-8"))
add_handlers(handlers, "html htm", lambda x: x.encode("utf-8")) add_handlers(handlers, "html htm", lambda x: x.encode("utf-8"))
add_handlers(handlers, "pyd pickle", pickle.dumps) add_handlers(handlers, "pyd pickle", pickle.dumps)
...@@ -152,7 +159,8 @@ def make_handlers(): ...@@ -152,7 +159,8 @@ def make_handlers():
add_handlers(handlers, "json jsn", lambda x: json.dumps(x).encode("utf-8")) add_handlers(handlers, "json jsn", lambda x: json.dumps(x).encode("utf-8"))
add_handlers(handlers, "mp msgpack msg", mp_dumps) add_handlers(handlers, "mp msgpack msg", mp_dumps)
add_handlers(handlers, "cbor", cbor_dumps) add_handlers(handlers, "cbor", cbor_dumps)
add_handlers(handlers, "jpg jpeg img image", lambda data: imageencoder(data, "jpg")) add_handlers(handlers, "jpg jpeg img image",
lambda data: imageencoder(data, "jpg"))
add_handlers(handlers, "png", lambda data: imageencoder(data, "png")) add_handlers(handlers, "png", lambda data: imageencoder(data, "png"))
add_handlers(handlers, "pbm", lambda data: imageencoder(data, "pbm")) add_handlers(handlers, "pbm", lambda data: imageencoder(data, "pbm"))
add_handlers(handlers, "pgm", lambda data: imageencoder(data, "pgm")) add_handlers(handlers, "pgm", lambda data: imageencoder(data, "pgm"))
...@@ -192,7 +200,8 @@ def encode_based_on_extension(sample: dict, handlers: dict): ...@@ -192,7 +200,8 @@ def encode_based_on_extension(sample: dict, handlers: dict):
:param handlers: handlers for encoding :param handlers: handlers for encoding
""" """
return { return {
k: encode_based_on_extension1(v, k, handlers) for k, v in list(sample.items()) k: encode_based_on_extension1(v, k, handlers)
for k, v in list(sample.items())
} }
...@@ -258,15 +267,14 @@ class TarWriter: ...@@ -258,15 +267,14 @@ class TarWriter:
""" """
def __init__( def __init__(
self, self,
fileobj, fileobj,
user: str = "bigdata", user: str="bigdata",
group: str = "bigdata", group: str="bigdata",
mode: int = 0o0444, mode: int=0o0444,
compress: Optional[bool] = None, compress: Optional[bool]=None,
encoder: Union[None, bool, Callable] = True, encoder: Union[None, bool, Callable]=True,
keep_meta: bool = False, keep_meta: bool=False, ):
):
"""Create a tar writer. """Create a tar writer.
:param fileobj: stream to write data to :param fileobj: stream to write data to
...@@ -330,8 +338,7 @@ class TarWriter: ...@@ -330,8 +338,7 @@ class TarWriter:
continue continue
if not isinstance(v, (bytes, bytearray, memoryview)): if not isinstance(v, (bytes, bytearray, memoryview)):
raise ValueError( raise ValueError(
f"{k} doesn't map to a bytes after encoding ({type(v)})" f"{k} doesn't map to a bytes after encoding ({type(v)})")
)
key = obj["__key__"] key = obj["__key__"]
for k in sorted(obj.keys()): for k in sorted(obj.keys()):
if k == "__key__": if k == "__key__":
...@@ -349,7 +356,8 @@ class TarWriter: ...@@ -349,7 +356,8 @@ class TarWriter:
ti.uname = self.user ti.uname = self.user
ti.gname = self.group ti.gname = self.group
if not isinstance(v, (bytes, bytearray, memoryview)): if not isinstance(v, (bytes, bytearray, memoryview)):
raise ValueError(f"converter didn't yield bytes: {k}, {type(v)}") raise ValueError(
f"converter didn't yield bytes: {k}, {type(v)}")
stream = io.BytesIO(v) stream = io.BytesIO(v)
self.tarstream.addfile(ti, stream) self.tarstream.addfile(ti, stream)
total += ti.size total += ti.size
...@@ -360,14 +368,13 @@ class ShardWriter: ...@@ -360,14 +368,13 @@ class ShardWriter:
"""Like TarWriter but splits into multiple shards.""" """Like TarWriter but splits into multiple shards."""
def __init__( def __init__(
self, self,
pattern: str, pattern: str,
maxcount: int = 100000, maxcount: int=100000,
maxsize: float = 3e9, maxsize: float=3e9,
post: Optional[Callable] = None, post: Optional[Callable]=None,
start_shard: int = 0, start_shard: int=0,
**kw, **kw, ):
):
"""Create a ShardWriter. """Create a ShardWriter.
:param pattern: output file pattern :param pattern: output file pattern
...@@ -400,8 +407,7 @@ class ShardWriter: ...@@ -400,8 +407,7 @@ class ShardWriter:
self.fname, self.fname,
self.count, self.count,
"%.1f GB" % (self.size / 1e9), "%.1f GB" % (self.size / 1e9),
self.total, self.total, )
)
self.shard += 1 self.shard += 1
stream = open(self.fname, "wb") stream = open(self.fname, "wb")
self.tarstream = TarWriter(stream, **self.kw) self.tarstream = TarWriter(stream, **self.kw)
...@@ -413,11 +419,8 @@ class ShardWriter: ...@@ -413,11 +419,8 @@ class ShardWriter:
:param obj: sample to be written :param obj: sample to be written
""" """
if ( if (self.tarstream is None or self.count >= self.maxcount or
self.tarstream is None self.size >= self.maxsize):
or self.count >= self.maxcount
or self.size >= self.maxsize
):
self.next_stream() self.next_stream()
size = self.tarstream.write(obj) size = self.tarstream.write(obj)
self.count += 1 self.count += 1
......
paddlespeech/audio/text/text_featurizer.py
浏览文件 @ 43af22e4
...@@ -17,6 +17,7 @@ from typing import Union ...@@ -17,6 +17,7 @@ from typing import Union
import sentencepiece as spm import sentencepiece as spm
from ..utils.log import Logger
from .utility import BLANK from .utility import BLANK
from .utility import EOS from .utility import EOS
from .utility import load_dict from .utility import load_dict
...@@ -24,7 +25,6 @@ from .utility import MASKCTC ...@@ -24,7 +25,6 @@ from .utility import MASKCTC
from .utility import SOS from .utility import SOS
from .utility import SPACE from .utility import SPACE
from .utility import UNK from .utility import UNK
from ..utils.log import Logger
logger = Logger(__name__) logger = Logger(__name__)
......
paddlespeech/audio/transform/perturb.py
浏览文件 @ 43af22e4
...@@ -12,15 +12,16 @@ ...@@ -12,15 +12,16 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
# Modified from espnet(https://github.com/espnet/espnet) # Modified from espnet(https://github.com/espnet/espnet)
import io
import os
import h5py
import librosa import librosa
import numpy import numpy
import numpy as np
import scipy import scipy
import soundfile import soundfile
import io
import os
import h5py
import numpy as np
class SoundHDF5File(): class SoundHDF5File():
"""Collecting sound files to a HDF5 file """Collecting sound files to a HDF5 file
...@@ -109,6 +110,7 @@ class SoundHDF5File(): ...@@ -109,6 +110,7 @@ class SoundHDF5File():
def close(self): def close(self):
self.file.close() self.file.close()
class SpeedPerturbation(): class SpeedPerturbation():
"""SpeedPerturbation """SpeedPerturbation
...@@ -558,4 +560,3 @@ class RIRConvolve(): ...@@ -558,4 +560,3 @@ class RIRConvolve():
[scipy.convolve(x, r, mode="same") for r in rir], axis=-1) [scipy.convolve(x, r, mode="same") for r in rir], axis=-1)
else: else:
return scipy.convolve(x, rir, mode="same") return scipy.convolve(x, rir, mode="same")
paddlespeech/audio/transform/spec_augment.py
浏览文件 @ 43af22e4
...@@ -14,6 +14,7 @@ ...@@ -14,6 +14,7 @@
# Modified from espnet(https://github.com/espnet/espnet) # Modified from espnet(https://github.com/espnet/espnet)
"""Spec Augment module for preprocessing i.e., data augmentation""" """Spec Augment module for preprocessing i.e., data augmentation"""
import random import random
import numpy import numpy
from PIL import Image from PIL import Image
......
paddlespeech/cli/executor.py
浏览文件 @ 43af22e4
...@@ -191,7 +191,7 @@ class BaseExecutor(ABC): ...@@ -191,7 +191,7 @@ class BaseExecutor(ABC):
line = line.strip() line = line.strip()
if not line: if not line:
continue continue
k, v = line.split() # space or \t k, v = line.split() # space or \t
job_contents[k] = v job_contents[k] = v
return job_contents return job_contents
......
paddlespeech/s2t/__init__.py
浏览文件 @ 43af22e4
...@@ -114,6 +114,7 @@ if not hasattr(paddle.Tensor, 'new_full'): ...@@ -114,6 +114,7 @@ if not hasattr(paddle.Tensor, 'new_full'):
paddle.Tensor.new_full = new_full paddle.Tensor.new_full = new_full
paddle.static.Variable.new_full = new_full paddle.static.Variable.new_full = new_full
def contiguous(xs: paddle.Tensor) -> paddle.Tensor: def contiguous(xs: paddle.Tensor) -> paddle.Tensor:
return xs return xs
......
paddlespeech/s2t/exps/u2/model.py
浏览文件 @ 43af22e4
...@@ -25,8 +25,6 @@ import paddle ...@@ -25,8 +25,6 @@ import paddle
from paddle import distributed as dist from paddle import distributed as dist
from paddlespeech.s2t.frontend.featurizer import TextFeaturizer from paddlespeech.s2t.frontend.featurizer import TextFeaturizer
from paddlespeech.s2t.io.dataloader import BatchDataLoader
from paddlespeech.s2t.io.dataloader import StreamDataLoader
from paddlespeech.s2t.io.dataloader import DataLoaderFactory from paddlespeech.s2t.io.dataloader import DataLoaderFactory
from paddlespeech.s2t.models.u2 import U2Model from paddlespeech.s2t.models.u2 import U2Model
from paddlespeech.s2t.training.optimizer import OptimizerFactory from paddlespeech.s2t.training.optimizer import OptimizerFactory
...@@ -109,7 +107,8 @@ class U2Trainer(Trainer): ...@@ -109,7 +107,8 @@ class U2Trainer(Trainer):
def valid(self): def valid(self):
self.model.eval() self.model.eval()
if not self.use_streamdata: if not self.use_streamdata:
logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}") logger.info(
f"Valid Total Examples: {len(self.valid_loader.dataset)}")
valid_losses = defaultdict(list) valid_losses = defaultdict(list)
num_seen_utts = 1 num_seen_utts = 1
total_loss = 0.0 total_loss = 0.0
...@@ -136,7 +135,8 @@ class U2Trainer(Trainer): ...@@ -136,7 +135,8 @@ class U2Trainer(Trainer):
msg += "epoch: {}, ".format(self.epoch) msg += "epoch: {}, ".format(self.epoch)
msg += "step: {}, ".format(self.iteration) msg += "step: {}, ".format(self.iteration)
if not self.use_streamdata: if not self.use_streamdata:
msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader)) msg += "batch: {}/{}, ".format(i + 1,
len(self.valid_loader))
msg += ', '.join('{}: {:>.6f}'.format(k, v) msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in valid_dump.items()) for k, v in valid_dump.items())
logger.info(msg) logger.info(msg)
...@@ -157,7 +157,8 @@ class U2Trainer(Trainer): ...@@ -157,7 +157,8 @@ class U2Trainer(Trainer):
self.before_train() self.before_train()
if not self.use_streamdata: if not self.use_streamdata:
logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}") logger.info(
f"Train Total Examples: {len(self.train_loader.dataset)}")
while self.epoch < self.config.n_epoch: while self.epoch < self.config.n_epoch:
with Timer("Epoch-Train Time Cost: {}"): with Timer("Epoch-Train Time Cost: {}"):
self.model.train() self.model.train()
...@@ -225,14 +226,18 @@ class U2Trainer(Trainer): ...@@ -225,14 +226,18 @@ class U2Trainer(Trainer):
config = self.config.clone() config = self.config.clone()
self.use_streamdata = config.get("use_stream_data", False) self.use_streamdata = config.get("use_stream_data", False)
if self.train: if self.train:
self.train_loader = DataLoaderFactory.get_dataloader('train', config, self.args) self.train_loader = DataLoaderFactory.get_dataloader(
self.valid_loader = DataLoaderFactory.get_dataloader('valid', config, self.args) 'train', config, self.args)
self.valid_loader = DataLoaderFactory.get_dataloader(
'valid', config, self.args)
logger.info("Setup train/valid Dataloader!") logger.info("Setup train/valid Dataloader!")
else: else:
decode_batch_size = config.get('decode', dict()).get( decode_batch_size = config.get('decode', dict()).get(
'decode_batch_size', 1) 'decode_batch_size', 1)
self.test_loader = DataLoaderFactory.get_dataloader('test', config, self.args) self.test_loader = DataLoaderFactory.get_dataloader('test', config,
self.align_loader = DataLoaderFactory.get_dataloader('align', config, self.args) self.args)
self.align_loader = DataLoaderFactory.get_dataloader(
'align', config, self.args)
logger.info("Setup test/align Dataloader!") logger.info("Setup test/align Dataloader!")
def setup_model(self): def setup_model(self):
......
paddlespeech/s2t/exps/u2_kaldi/model.py
浏览文件 @ 43af22e4
...@@ -105,7 +105,8 @@ class U2Trainer(Trainer): ...@@ -105,7 +105,8 @@ class U2Trainer(Trainer):
def valid(self): def valid(self):
self.model.eval() self.model.eval()
if not self.use_streamdata: if not self.use_streamdata:
logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}") logger.info(
f"Valid Total Examples: {len(self.valid_loader.dataset)}")
valid_losses = defaultdict(list) valid_losses = defaultdict(list)
num_seen_utts = 1 num_seen_utts = 1
total_loss = 0.0 total_loss = 0.0
...@@ -133,7 +134,8 @@ class U2Trainer(Trainer): ...@@ -133,7 +134,8 @@ class U2Trainer(Trainer):
msg += "epoch: {}, ".format(self.epoch) msg += "epoch: {}, ".format(self.epoch)
msg += "step: {}, ".format(self.iteration) msg += "step: {}, ".format(self.iteration)
if not self.use_streamdata: if not self.use_streamdata:
msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader)) msg += "batch: {}/{}, ".format(i + 1,
len(self.valid_loader))
msg += ', '.join('{}: {:>.6f}'.format(k, v) msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in valid_dump.items()) for k, v in valid_dump.items())
logger.info(msg) logger.info(msg)
...@@ -153,7 +155,8 @@ class U2Trainer(Trainer): ...@@ -153,7 +155,8 @@ class U2Trainer(Trainer):
self.before_train() self.before_train()
if not self.use_streamdata: if not self.use_streamdata:
logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}") logger.info(
f"Train Total Examples: {len(self.train_loader.dataset)}")
while self.epoch < self.config.n_epoch: while self.epoch < self.config.n_epoch:
with Timer("Epoch-Train Time Cost: {}"): with Timer("Epoch-Train Time Cost: {}"):
self.model.train() self.model.train()
...@@ -165,8 +168,8 @@ class U2Trainer(Trainer): ...@@ -165,8 +168,8 @@ class U2Trainer(Trainer):
msg += "epoch: {}, ".format(self.epoch) msg += "epoch: {}, ".format(self.epoch)
msg += "step: {}, ".format(self.iteration) msg += "step: {}, ".format(self.iteration)
if not self.use_streamdata: if not self.use_streamdata:
msg += "batch : {}/{}, ".format(batch_index + 1, msg += "batch : {}/{}, ".format(
len(self.train_loader)) batch_index + 1, len(self.train_loader))
msg += "lr: {:>.8f}, ".format(self.lr_scheduler()) msg += "lr: {:>.8f}, ".format(self.lr_scheduler())
msg += "data time: {:>.3f}s, ".format(dataload_time) msg += "data time: {:>.3f}s, ".format(dataload_time)
self.train_batch(batch_index, batch, msg) self.train_batch(batch_index, batch, msg)
...@@ -204,21 +207,24 @@ class U2Trainer(Trainer): ...@@ -204,21 +207,24 @@ class U2Trainer(Trainer):
self.use_streamdata = config.get("use_stream_data", False) self.use_streamdata = config.get("use_stream_data", False)
if self.train: if self.train:
config = self.config.clone() config = self.config.clone()
self.train_loader = DataLoaderFactory.get_dataloader('train', config, self.args) self.train_loader = DataLoaderFactory.get_dataloader(
'train', config, self.args)
config = self.config.clone() config = self.config.clone()
config['preprocess_config'] = None config['preprocess_config'] = None
self.valid_loader = DataLoaderFactory.get_dataloader('valid', config, self.args) self.valid_loader = DataLoaderFactory.get_dataloader(
'valid', config, self.args)
logger.info("Setup train/valid Dataloader!") logger.info("Setup train/valid Dataloader!")
else: else:
config = self.config.clone() config = self.config.clone()
config['preprocess_config'] = None config['preprocess_config'] = None
self.test_loader = DataLoaderFactory.get_dataloader('test', config, self.args) self.test_loader = DataLoaderFactory.get_dataloader('test', config,
self.args)
config = self.config.clone() config = self.config.clone()
config['preprocess_config'] = None config['preprocess_config'] = None
self.align_loader = DataLoaderFactory.get_dataloader('align', config, self.args) self.align_loader = DataLoaderFactory.get_dataloader(
'align', config, self.args)
logger.info("Setup test/align Dataloader!") logger.info("Setup test/align Dataloader!")
def setup_model(self): def setup_model(self):
config = self.config config = self.config
......
paddlespeech/s2t/exps/u2_st/model.py
浏览文件 @ 43af22e4
...@@ -121,7 +121,8 @@ class U2STTrainer(Trainer): ...@@ -121,7 +121,8 @@ class U2STTrainer(Trainer):
def valid(self): def valid(self):
self.model.eval() self.model.eval()
if not self.use_streamdata: if not self.use_streamdata:
logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}") logger.info(
f"Valid Total Examples: {len(self.valid_loader.dataset)}")
valid_losses = defaultdict(list) valid_losses = defaultdict(list)
num_seen_utts = 1 num_seen_utts = 1
total_loss = 0.0 total_loss = 0.0
...@@ -155,7 +156,8 @@ class U2STTrainer(Trainer): ...@@ -155,7 +156,8 @@ class U2STTrainer(Trainer):
msg += "epoch: {}, ".format(self.epoch) msg += "epoch: {}, ".format(self.epoch)
msg += "step: {}, ".format(self.iteration) msg += "step: {}, ".format(self.iteration)
if not self.use_streamdata: if not self.use_streamdata:
msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader)) msg += "batch: {}/{}, ".format(i + 1,
len(self.valid_loader))
msg += ', '.join('{}: {:>.6f}'.format(k, v) msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in valid_dump.items()) for k, v in valid_dump.items())
logger.info(msg) logger.info(msg)
...@@ -175,7 +177,8 @@ class U2STTrainer(Trainer): ...@@ -175,7 +177,8 @@ class U2STTrainer(Trainer):
self.before_train() self.before_train()
if not self.use_streamdata: if not self.use_streamdata:
logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}") logger.info(
f"Train Total Examples: {len(self.train_loader.dataset)}")
while self.epoch < self.config.n_epoch: while self.epoch < self.config.n_epoch:
with Timer("Epoch-Train Time Cost: {}"): with Timer("Epoch-Train Time Cost: {}"):
self.model.train() self.model.train()
...@@ -248,14 +251,16 @@ class U2STTrainer(Trainer): ...@@ -248,14 +251,16 @@ class U2STTrainer(Trainer):
config['load_transcript'] = load_transcript config['load_transcript'] = load_transcript
self.use_streamdata = config.get("use_stream_data", False) self.use_streamdata = config.get("use_stream_data", False)
if self.train: if self.train:
self.train_loader = DataLoaderFactory.get_dataloader('train', config, self.args) self.train_loader = DataLoaderFactory.get_dataloader(
self.valid_loader = DataLoaderFactory.get_dataloader('valid', config, self.args) 'train', config, self.args)
self.valid_loader = DataLoaderFactory.get_dataloader(
'valid', config, self.args)
logger.info("Setup train/valid Dataloader!") logger.info("Setup train/valid Dataloader!")
else: else:
self.test_loader = DataLoaderFactory.get_dataloader('test', config, self.args) self.test_loader = DataLoaderFactory.get_dataloader('test', config,
self.args)
logger.info("Setup test Dataloader!") logger.info("Setup test Dataloader!")
def setup_model(self): def setup_model(self):
config = self.config config = self.config
model_conf = config model_conf = config
......
paddlespeech/s2t/io/dataloader.py
浏览文件 @ 43af22e4
...@@ -22,17 +22,16 @@ import paddle ...@@ -22,17 +22,16 @@ import paddle
from paddle.io import BatchSampler from paddle.io import BatchSampler
from paddle.io import DataLoader from paddle.io import DataLoader
from paddle.io import DistributedBatchSampler from paddle.io import DistributedBatchSampler
from yacs.config import CfgNode
import paddlespeech.audio.streamdata as streamdata
from paddlespeech.audio.text.text_featurizer import TextFeaturizer
from paddlespeech.s2t.io.batchfy import make_batchset from paddlespeech.s2t.io.batchfy import make_batchset
from paddlespeech.s2t.io.converter import CustomConverter from paddlespeech.s2t.io.converter import CustomConverter
from paddlespeech.s2t.io.dataset import TransformDataset from paddlespeech.s2t.io.dataset import TransformDataset
from paddlespeech.s2t.io.reader import LoadInputsAndTargets from paddlespeech.s2t.io.reader import LoadInputsAndTargets
from paddlespeech.s2t.utils.log import Log from paddlespeech.s2t.utils.log import Log
import paddlespeech.audio.streamdata as streamdata
from paddlespeech.audio.text.text_featurizer import TextFeaturizer
from yacs.config import CfgNode
__all__ = ["BatchDataLoader", "StreamDataLoader"] __all__ = ["BatchDataLoader", "StreamDataLoader"]
logger = Log(__name__).getlog() logger = Log(__name__).getlog()
...@@ -61,6 +60,7 @@ def batch_collate(x): ...@@ -61,6 +60,7 @@ def batch_collate(x):
""" """
return x[0] return x[0]
def read_preprocess_cfg(preprocess_conf_file): def read_preprocess_cfg(preprocess_conf_file):
augment_conf = dict() augment_conf = dict()
preprocess_cfg = CfgNode(new_allowed=True) preprocess_cfg = CfgNode(new_allowed=True)
...@@ -82,7 +82,8 @@ def read_preprocess_cfg(preprocess_conf_file): ...@@ -82,7 +82,8 @@ def read_preprocess_cfg(preprocess_conf_file):
augment_conf['num_t_mask'] = process['n_mask'] augment_conf['num_t_mask'] = process['n_mask']
augment_conf['t_inplace'] = process['inplace'] augment_conf['t_inplace'] = process['inplace']
augment_conf['t_replace_with_zero'] = process['replace_with_zero'] augment_conf['t_replace_with_zero'] = process['replace_with_zero']
return augment_conf return augment_conf
class StreamDataLoader(): class StreamDataLoader():
def __init__(self, def __init__(self,
...@@ -95,12 +96,12 @@ class StreamDataLoader(): ...@@ -95,12 +96,12 @@ class StreamDataLoader():
frame_length=25, frame_length=25,
frame_shift=10, frame_shift=10,
dither=0.0, dither=0.0,
minlen_in: float=0.0, minlen_in: float=0.0,
maxlen_in: float=float('inf'), maxlen_in: float=float('inf'),
minlen_out: float=0.0, minlen_out: float=0.0,
maxlen_out: float=float('inf'), maxlen_out: float=float('inf'),
resample_rate: int=16000, resample_rate: int=16000,
shuffle_size: int=10000, shuffle_size: int=10000,
sort_size: int=1000, sort_size: int=1000,
n_iter_processes: int=1, n_iter_processes: int=1,
prefetch_factor: int=2, prefetch_factor: int=2,
...@@ -116,11 +117,11 @@ class StreamDataLoader(): ...@@ -116,11 +117,11 @@ class StreamDataLoader():
text_featurizer = TextFeaturizer(unit_type, vocab_filepath) text_featurizer = TextFeaturizer(unit_type, vocab_filepath)
symbol_table = text_featurizer.vocab_dict symbol_table = text_featurizer.vocab_dict
self.feat_dim = num_mel_bins self.feat_dim = num_mel_bins
self.vocab_size = text_featurizer.vocab_size self.vocab_size = text_featurizer.vocab_size
augment_conf = read_preprocess_cfg(preprocess_conf) augment_conf = read_preprocess_cfg(preprocess_conf)
# The list of shard # The list of shard
shardlist = [] shardlist = []
with open(manifest_file, "r") as f: with open(manifest_file, "r") as f:
...@@ -128,58 +129,68 @@ class StreamDataLoader(): ...@@ -128,58 +129,68 @@ class StreamDataLoader():
shardlist.append(line.strip()) shardlist.append(line.strip())
world_size = 1 world_size = 1
try: try:
world_size = paddle.distributed.get_world_size() world_size = paddle.distributed.get_world_size()
except Exception as e: except Exception as e:
logger.warninig(e) logger.warninig(e)
logger.warninig("can not get world_size using paddle.distributed.get_world_size(), use world_size=1") logger.warninig(
assert(len(shardlist) >= world_size, "the length of shard list should >= number of gpus/xpus/...") "can not get world_size using paddle.distributed.get_world_size(), use world_size=1"
)
assert len(shardlist) >= world_size, \
"the length of shard list should >= number of gpus/xpus/..."
update_n_iter_processes = int(max(min(len(shardlist)/world_size - 1, self.n_iter_processes), 0)) update_n_iter_processes = int(
max(min(len(shardlist) / world_size - 1, self.n_iter_processes), 0))
logger.info(f"update_n_iter_processes {update_n_iter_processes}") logger.info(f"update_n_iter_processes {update_n_iter_processes}")
if update_n_iter_processes != self.n_iter_processes: if update_n_iter_processes != self.n_iter_processes:
self.n_iter_processes = update_n_iter_processes self.n_iter_processes = update_n_iter_processes
logger.info(f"change nun_workers to {self.n_iter_processes}") logger.info(f"change nun_workers to {self.n_iter_processes}")
if self.dist_sampler: if self.dist_sampler:
base_dataset = streamdata.DataPipeline( base_dataset = streamdata.DataPipeline(
streamdata.SimpleShardList(shardlist), streamdata.SimpleShardList(shardlist), streamdata.split_by_node
streamdata.split_by_node if train_mode else streamdata.placeholder(), if train_mode else streamdata.placeholder(),
streamdata.split_by_worker, streamdata.split_by_worker,
streamdata.tarfile_to_samples(streamdata.reraise_exception) streamdata.tarfile_to_samples(streamdata.reraise_exception))
)
else: else:
base_dataset = streamdata.DataPipeline( base_dataset = streamdata.DataPipeline(
streamdata.SimpleShardList(shardlist), streamdata.SimpleShardList(shardlist),
streamdata.split_by_worker, streamdata.split_by_worker,
streamdata.tarfile_to_samples(streamdata.reraise_exception) streamdata.tarfile_to_samples(streamdata.reraise_exception))
)
self.dataset = base_dataset.append_list( self.dataset = base_dataset.append_list(
streamdata.audio_tokenize(symbol_table), streamdata.audio_tokenize(symbol_table),
streamdata.audio_data_filter(frame_shift=frame_shift, max_length=maxlen_in, min_length=minlen_in, token_max_length=maxlen_out, token_min_length=minlen_out), streamdata.audio_data_filter(
frame_shift=frame_shift,
max_length=maxlen_in,
min_length=minlen_in,
token_max_length=maxlen_out,
token_min_length=minlen_out),
streamdata.audio_resample(resample_rate=resample_rate), streamdata.audio_resample(resample_rate=resample_rate),
streamdata.audio_compute_fbank(num_mel_bins=num_mel_bins, frame_length=frame_length, frame_shift=frame_shift, dither=dither), streamdata.audio_compute_fbank(
streamdata.audio_spec_aug(**augment_conf) if train_mode else streamdata.placeholder(), # num_t_mask=2, num_f_mask=2, max_t=40, max_f=30, max_w=80) num_mel_bins=num_mel_bins,
frame_length=frame_length,
frame_shift=frame_shift,
dither=dither),
streamdata.audio_spec_aug(**augment_conf)
if train_mode else streamdata.placeholder(
), # num_t_mask=2, num_f_mask=2, max_t=40, max_f=30, max_w=80)
streamdata.shuffle(shuffle_size), streamdata.shuffle(shuffle_size),
streamdata.sort(sort_size=sort_size), streamdata.sort(sort_size=sort_size),
streamdata.batched(batch_size), streamdata.batched(batch_size),
streamdata.audio_padding(), streamdata.audio_padding(),
streamdata.audio_cmvn(cmvn_file) streamdata.audio_cmvn(cmvn_file))
)
if paddle.__version__ >= '2.3.2': if paddle.__version__ >= '2.3.2':
self.loader = streamdata.WebLoader( self.loader = streamdata.WebLoader(
self.dataset, self.dataset,
num_workers=self.n_iter_processes, num_workers=self.n_iter_processes,
prefetch_factor = self.prefetch_factor, prefetch_factor=self.prefetch_factor,
batch_size=None batch_size=None)
)
else: else:
self.loader = streamdata.WebLoader( self.loader = streamdata.WebLoader(
self.dataset, self.dataset,
num_workers=self.n_iter_processes, num_workers=self.n_iter_processes,
batch_size=None batch_size=None)
)
def __iter__(self): def __iter__(self):
return self.loader.__iter__() return self.loader.__iter__()
...@@ -188,7 +199,9 @@ class StreamDataLoader(): ...@@ -188,7 +199,9 @@ class StreamDataLoader():
return self.__iter__() return self.__iter__()
def __len__(self): def __len__(self):
logger.info("Stream dataloader does not support calculate the length of the dataset") logger.info(
"Stream dataloader does not support calculate the length of the dataset"
)
return -1 return -1
...@@ -347,7 +360,7 @@ class DataLoaderFactory(): ...@@ -347,7 +360,7 @@ class DataLoaderFactory():
config['train_mode'] = True config['train_mode'] = True
elif mode == 'valid': elif mode == 'valid':
config['manifest'] = config.dev_manifest config['manifest'] = config.dev_manifest
config['train_mode'] = False config['train_mode'] = False
elif model == 'test' or mode == 'align': elif model == 'test' or mode == 'align':
config['manifest'] = config.test_manifest config['manifest'] = config.test_manifest
config['train_mode'] = False config['train_mode'] = False
...@@ -358,30 +371,31 @@ class DataLoaderFactory(): ...@@ -358,30 +371,31 @@ class DataLoaderFactory():
config['maxlen_out'] = float('inf') config['maxlen_out'] = float('inf')
config['dist_sampler'] = False config['dist_sampler'] = False
else: else:
raise KeyError("not valid mode type!!, please input one of 'train, valid, test, align'") raise KeyError(
return StreamDataLoader( "not valid mode type!!, please input one of 'train, valid, test, align'"
manifest_file=config.manifest,
train_mode=config.train_mode,
unit_type=config.unit_type,
preprocess_conf=config.preprocess_config,
batch_size=config.batch_size,
num_mel_bins=config.feat_dim,
frame_length=config.window_ms,
frame_shift=config.stride_ms,
dither=config.dither,
minlen_in=config.minlen_in,
maxlen_in=config.maxlen_in,
minlen_out=config.minlen_out,
maxlen_out=config.maxlen_out,
resample_rate=config.resample_rate,
shuffle_size=config.shuffle_size,
sort_size=config.sort_size,
n_iter_processes=config.num_workers,
prefetch_factor=config.prefetch_factor,
dist_sampler=config.dist_sampler,
cmvn_file=config.cmvn_file,
vocab_filepath=config.vocab_filepath,
) )
return StreamDataLoader(
manifest_file=config.manifest,
train_mode=config.train_mode,
unit_type=config.unit_type,
preprocess_conf=config.preprocess_config,
batch_size=config.batch_size,
num_mel_bins=config.feat_dim,
frame_length=config.window_ms,
frame_shift=config.stride_ms,
dither=config.dither,
minlen_in=config.minlen_in,
maxlen_in=config.maxlen_in,
minlen_out=config.minlen_out,
maxlen_out=config.maxlen_out,
resample_rate=config.resample_rate,
shuffle_size=config.shuffle_size,
sort_size=config.sort_size,
n_iter_processes=config.num_workers,
prefetch_factor=config.prefetch_factor,
dist_sampler=config.dist_sampler,
cmvn_file=config.cmvn_file,
vocab_filepath=config.vocab_filepath, )
else: else:
if mode == 'train': if mode == 'train':
config['manifest'] = config.train_manifest config['manifest'] = config.train_manifest
...@@ -411,7 +425,7 @@ class DataLoaderFactory(): ...@@ -411,7 +425,7 @@ class DataLoaderFactory():
config['train_mode'] = False config['train_mode'] = False
config['sortagrad'] = False config['sortagrad'] = False
config['batch_size'] = config.get('decode', dict()).get( config['batch_size'] = config.get('decode', dict()).get(
'decode_batch_size', 1) 'decode_batch_size', 1)
config['maxlen_in'] = float('inf') config['maxlen_in'] = float('inf')
config['maxlen_out'] = float('inf') config['maxlen_out'] = float('inf')
config['minibatches'] = 0 config['minibatches'] = 0
...@@ -427,8 +441,10 @@ class DataLoaderFactory(): ...@@ -427,8 +441,10 @@ class DataLoaderFactory():
config['dist_sampler'] = False config['dist_sampler'] = False
config['shortest_first'] = False config['shortest_first'] = False
else: else:
raise KeyError("not valid mode type!!, please input one of 'train, valid, test, align'") raise KeyError(
"not valid mode type!!, please input one of 'train, valid, test, align'"
)
return BatchDataLoader( return BatchDataLoader(
json_file=config.manifest, json_file=config.manifest,
train_mode=config.train_mode, train_mode=config.train_mode,
...@@ -450,4 +466,3 @@ class DataLoaderFactory(): ...@@ -450,4 +466,3 @@ class DataLoaderFactory():
num_encs=config.num_encs, num_encs=config.num_encs,
dist_sampler=config.dist_sampler, dist_sampler=config.dist_sampler,
shortest_first=config.shortest_first) shortest_first=config.shortest_first)
paddlespeech/s2t/modules/align.py
浏览文件 @ 43af22e4
此差异已折叠。
paddlespeech/s2t/modules/encoder.py
浏览文件 @ 43af22e4
...@@ -255,6 +255,7 @@ class BaseEncoder(nn.Layer): ...@@ -255,6 +255,7 @@ class BaseEncoder(nn.Layer):
xs, xs,
att_mask, att_mask,
pos_emb, pos_emb,
mask_pad=paddle.ones([0, 0, 0], dtype=paddle.bool),
att_cache=att_cache[i:i + 1] if elayers > 0 else att_cache, att_cache=att_cache[i:i + 1] if elayers > 0 else att_cache,
cnn_cache=cnn_cache[i:i + 1] cnn_cache=cnn_cache[i:i + 1]
if paddle.shape(cnn_cache)[0] > 0 else cnn_cache, ) if paddle.shape(cnn_cache)[0] > 0 else cnn_cache, )
......
paddlespeech/s2t/modules/encoder_layer.py
浏览文件 @ 43af22e4
...@@ -195,8 +195,7 @@ class ConformerEncoderLayer(nn.Layer): ...@@ -195,8 +195,7 @@ class ConformerEncoderLayer(nn.Layer):
x: paddle.Tensor, x: paddle.Tensor,
mask: paddle.Tensor, mask: paddle.Tensor,
pos_emb: paddle.Tensor, pos_emb: paddle.Tensor,
mask_pad: paddle. mask_pad: paddle.Tensor, #paddle.ones([0, 0, 0],dtype=paddle.bool)
Tensor, # paddle.ones([0, 0, 0], dtype=paddle.bool)
att_cache: paddle.Tensor, # paddle.zeros([0, 0, 0, 0]) att_cache: paddle.Tensor, # paddle.zeros([0, 0, 0, 0])
cnn_cache: paddle.Tensor, # paddle.zeros([0, 0, 0, 0]) cnn_cache: paddle.Tensor, # paddle.zeros([0, 0, 0, 0])
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]: ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]:
......
paddlespeech/s2t/modules/initializer.py
浏览文件 @ 43af22e4
...@@ -11,7 +11,7 @@ ...@@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import numpy as np
class DefaultInitializerContext(object): class DefaultInitializerContext(object):
""" """
......
paddlespeech/t2s/exps/syn_utils.py
浏览文件 @ 43af22e4
此差异已折叠。
paddlespeech/t2s/exps/vits/train.py
浏览文件 @ 43af22e4
此差异已折叠。
paddlespeech/t2s/exps/vits/voice_cloning.py 0 → 100644
浏览文件 @ 43af22e4
此差异已折叠。
setup.py
浏览文件 @ 43af22e4
此差异已折叠。
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