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ERNIE

PaddlePaddle / ERNIE
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ERNIE
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ernie-sat/README.md
浏览文件 @ c2f4709e
......@@ -11,7 +11,7 @@ ERNIE-SAT中我们提出了两项创新：
### 1.安装飞桨
我们的代码基于 Paddle(version>=2.0)
本项目的代码基于 Paddle(version>=2.0)
### 2.预训练模型
......@@ -23,7 +23,7 @@ ERNIE-SAT中我们提出了两项创新：
### 3.下载
1. 我们使用parallel wavegan作为声码器(vocoder):
1. 本项目使用parallel wavegan作为声码器(vocoder):
- [pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip)
创建download文件夹，下载上述预训练的声码器(vocoder)模型并将其解压
......@@ -34,7 +34,7 @@ cd download
unzip pwg_aishell3_ckpt_0.5.zip
```
2. 我们使用[FastSpeech2](https://arxiv.org/abs/2006.04558) 作为音素(phoneme)的持续时间预测器:
2. 本项目使用[FastSpeech2](https://arxiv.org/abs/2006.04558) 作为音素(phoneme)的持续时间预测器:
- [fastspeech2_conformer_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_conformer_baker_ckpt_0.5.zip) 中文场景下使用
- [fastspeech2_nosil_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_ljspeech_ckpt_0.5.zip) 英文场景下使用
......@@ -48,7 +48,7 @@ unzip fastspeech2_nosil_ljspeech_ckpt_0.5.zip
### 4.推理
我们目前开源了语音编辑、个性化语音合成、跨语言语音合成的推理代码，后续会逐步开源。
本项目当前开源了语音编辑、个性化语音合成、跨语言语音合成的推理代码，后续会逐步开源。
注：当前采用的声码器版本与[模型训练时版本](https://github.com/kan-bayashi/ParallelWaveGAN)在英文上存在差异，您可使用模型训练时版本作为您的声码器，模型将在后续更新中升级。
我们提供特定音频文件, 以及其对应的文本、音素相关文件:
......
ernie-sat/sedit_inference_0520.py → ernie-sat/inference.py
浏览文件 @ c2f4709e
#!/usr/bin/env python3
"""Script to run the inference of text-to-speeech model."""
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
from parallel_wavegan.utils import download_pretrained_model
from pathlib import Path
import paddle
import soundfile
import os
import math
import string
import numpy as np
from espnet2.tasks.mlm import MLMTask
from read_text import read_2column_text,load_num_sequence_text
from util import sentence2phns,get_voc_out, evaluate_durations
from utils import sentence2phns,get_voc_out, evaluate_durations
import librosa
import random
from ipywidgets import widgets
import IPython.display as ipd
import soundfile as sf
import sys
import pickle
......@@ -37,19 +27,10 @@ from typing import Union
from paddlespeech.t2s.datasets.get_feats import LogMelFBank
from paddlespeech.t2s.modules.nets_utils import make_non_pad_mask
duration_path_dict = {
"ljspeech":"/mnt/home/v_baihe/projects/espnet/egs2/ljspeech/tts1/exp/kan-bayashi/ljspeech_tts_train_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave/train.loss.ave_5best.pth",
"vctk": "/mnt/home/v_baihe/projects/espnet/egs2/vctk/tts1/exp/kan-bayashi/vctk_tts_train_gst+xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave/train.loss.ave_5best.pth",
# "ljspeech":"/home/mnt2/zz/workspace/work/espnet_richard_infer/egs2/ljspeech/tts1/exp/kan-bayashi/ljspeech_tts_train_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave/train.loss.ave_5best.pth",
# "vctk": "/home/mnt2/zz/workspace/work/espnet_richard_infer/egs2/vctk/tts1/exp/kan-bayashi/vctk_tts_train_gst+xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave/train.loss.ave_5best.pth",
"vctk_unseen":"/mnt/home/v_baihe/projects/espnet/egs2/vctk/tts1/exp/tts_train_fs2_raw_phn_tacotron_g2p_en_no_space/train.loss.ave_5best.pth",
"libritts":"/mnt/home/v_baihe/projects/espnet/egs2/libritts/tts1/exp/kan-bayashi/libritts_tts_train_gst+xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss/train.loss.ave_5best.pth"
}
random.seed(0)
np.random.seed(0)
def plot_mel_and_vocode_wav(uid, prefix, clone_uid, clone_prefix, source_language, target_language, model_name, wav_path,full_origin_str, old_str, new_str, vocoder,duration_preditor_path,sid=None, non_autoreg=True):
wav_org, input_feat, output_feat, old_span_boundary, new_span_boundary, fs, hop_length = get_mlm_output(
uid,
......@@ -67,46 +48,46 @@ def plot_mel_and_vocode_wav(uid, prefix, clone_uid, clone_prefix, source_languag
sid=sid
)
masked_feat = output_feat[new_span_boundary[0]:new_span_boundary[1]].detach().float().cpu().numpy()
if target_language == 'english':
output_feat_np = output_feat.detach().float().cpu().numpy()
replaced_wav_paddle_voc = get_voc_out(output_feat_np, target_language)
replaced_wav = replaced_wav_paddle_voc
elif target_language == 'chinese':
assert old_span_boundary[1] == new_span_boundary[0], "old_span_boundary[1] is not same with new_span_boundary[0]."
output_feat_np = output_feat.detach().float().cpu().numpy()
replaced_wav = get_voc_out(output_feat_np)
replaced_wav_only_mask = get_voc_out(masked_feat)
replaced_wav_only_mask_fst2_voc = get_voc_out(masked_feat, target_language)
old_time_boundary = [hop_length * x for x in old_span_boundary]
new_time_boundary = [hop_length * x for x in new_span_boundary]
wav_org_replaced = np.concatenate([wav_org[:old_time_boundary[0]], replaced_wav[new_time_boundary[0]:new_time_boundary[1]], wav_org[old_time_boundary[1]:]])
if target_language == 'english':
# new add to test paddle vocoder
wav_org_replaced_paddle_voc = np.concatenate([wav_org[:old_time_boundary[0]], replaced_wav_paddle_voc[new_time_boundary[0]:new_time_boundary[1]], wav_org[old_time_boundary[1]:]])
data_dict = {"origin":wav_org,
data_dict = {
"origin":wav_org,
"output":wav_org_replaced_paddle_voc}
elif target_language == 'chinese':
wav_org_replaced_only_mask = np.concatenate([wav_org[:old_time_boundary[0]], replaced_wav_only_mask, wav_org[old_time_boundary[1]:]])
wav_org_replaced_only_mask_fst2_voc = np.concatenate([wav_org[:old_time_boundary[0]], replaced_wav_only_mask_fst2_voc, wav_org[old_time_boundary[1]:]])
data_dict = {"origin":wav_org,
data_dict = {
"origin":wav_org,
"output": wav_org_replaced_only_mask_fst2_voc,}
return data_dict, old_span_boundary
def load_vocoder(vocoder_tag="parallel_wavegan/libritts_parallel_wavegan.v1"):
vocoder_tag = vocoder_tag.replace("parallel_wavegan/", "")
vocoder_file = download_pretrained_model(vocoder_tag)
vocoder_config = Path(vocoder_file).parent / "config.yml"
vocoder = TTSTask.build_vocoder_from_file(
vocoder_config, vocoder_file, None, 'cpu'
)
return vocoder
def load_model(model_name):
config_path='./pretrained_model/{}/config.yaml'.format(model_name)
model_path = './pretrained_model/{}/model.pdparams'.format(model_name)
......@@ -132,12 +113,6 @@ def get_align_data(uid,prefix):
return mfa_text, mfa_start, mfa_end, mfa_wav_path
def get_fs2_model(model_name):
model, config = TTSTask.build_model_from_file(model_file=model_name)
processor = TTSTask.build_preprocess_fn(config, train=False)
return model, processor
def get_masked_mel_boundary(mfa_start, mfa_end, fs, hop_length, span_tobe_replaced):
align_start=paddle.to_tensor(mfa_start).unsqueeze(0)
align_end =paddle.to_tensor(mfa_end).unsqueeze(0)
......@@ -150,6 +125,16 @@ def get_masked_mel_boundary(mfa_start, mfa_end, fs, hop_length, span_tobe_replac
return span_boundary
def gen_phns(zh_mapping, phns):
new_phns = []
for x in phns:
if x in zh_mapping.keys():
new_phns.extend(zh_mapping[x].split(" "))
else:
new_phns.extend(['<unk>'])
return new_phns
def get_mapping(phn_mapping="./phn_mapping.txt"):
zh_mapping = {}
with open(phn_mapping, "r") as f:
......@@ -160,15 +145,6 @@ def get_mapping(phn_mapping="./phn_mapping.txt"):
return zh_mapping
def gen_phns(zh_mapping, phns):
new_phns = []
for x in phns:
if x in zh_mapping.keys():
new_phns.extend(zh_mapping[x].split(" "))
else:
new_phns.extend(['<unk>'])
return new_phns
def get_phns_and_spans_paddle(uid, prefix, old_str, new_str, source_language, target_language):
zh_mapping = get_mapping()
old_str = old_str.strip()
......@@ -205,6 +181,7 @@ def get_phns_and_spans_paddle(uid, prefix, old_str, new_str, source_language, ta
assert target_language == "chinese" or target_language == "english", "cloning is not support for this language, please check it."
else:
if source_language == target_language and target_language == "english":
new_phns_origin = old_phns
new_phns_append, _ = sentence2phns(new_str_append, "en")
......@@ -223,10 +200,11 @@ def get_phns_and_spans_paddle(uid, prefix, old_str, new_str, source_language, ta
span_tobe_replaced = [len(old_phns),len(old_phns)]
span_tobe_added = [len(old_phns),len(new_phns)]
else:
if source_language == target_language and target_language == "english":
new_phns, _ = sentence2phns(new_str, "en")
# 纯中文
elif source_language == target_language and target_language == "chinese":
new_phns, _ = sentence2phns(new_str, "zh")
new_phns = gen_phns(zh_mapping, new_phns)
......@@ -247,7 +225,6 @@ def get_phns_and_spans_paddle(uid, prefix, old_str, new_str, source_language, ta
left_index = 0
sp_count = 0
# find the left different index
for idx, phn in enumerate(old_phns):
if phn == "sp":
sp_count += 1
......@@ -293,8 +270,8 @@ def get_phns_and_spans_paddle(uid, prefix, old_str, new_str, source_language, ta
new_phns = new_phns_left+new_phns_middle+new_phns_right
return mfa_start, mfa_end, old_phns, new_phns, span_tobe_replaced, span_tobe_added
return mfa_start, mfa_end, old_phns, new_phns, span_tobe_replaced, span_tobe_added
......@@ -311,10 +288,12 @@ def duration_adjust_factor(original_dur, pred_dur, phns):
factor_list.sort()
if len(factor_list)<5:
return 1
length = 2
return np.average(factor_list[length:-length])
def prepare_features_with_duration(uid, prefix, clone_uid, clone_prefix, source_language, target_language, mlm_model, old_str, new_str, wav_path,duration_preditor_path,sid=None, mask_reconstruct=False,duration_adjust=True,start_end_sp=False, train_args=None):
wav_org, rate = librosa.load(wav_path, sr=train_args.feats_extract_conf['fs'])
fs = train_args.feats_extract_conf['fs']
......@@ -331,8 +310,10 @@ def prepare_features_with_duration(uid, prefix, clone_uid, clone_prefix, source_
old_durations = evaluate_durations(old_phns, target_language=target_language)
elif target_language =="chinese":
if source_language == "english":
old_durations = evaluate_durations(old_phns, target_language=source_language)
elif source_language == "chinese":
old_durations = evaluate_durations(old_phns, target_language=source_language)
......@@ -353,8 +334,7 @@ def prepare_features_with_duration(uid, prefix, clone_uid, clone_prefix, source_
if duration_adjust:
d_factor = duration_adjust_factor(original_old_durations,old_durations, old_phns)
d_factor_paddle = duration_adjust_factor(original_old_durations,old_durations, old_phns)
if target_language =="chinese":
d_factor = d_factor * 1.35
d_factor = d_factor * 1.25
else:
d_factor = 1
......@@ -414,7 +394,7 @@ mask_reconstruct=False, train_args=None):
align_end =np.array(mfa_end)
token_to_id = {item: i for i, item in enumerate(train_args.token_list)}
text = np.array(list(map(lambda x: token_to_id.get(x, token_to_id['<unk>']), phns_list)))
print('unk id is', token_to_id['<unk>'])
# print('unk id is', token_to_id['<unk>'])
# text = np.array(processor(uid='1', data={'text':" ".join(phns_list)})['text'])
span_boundary = np.array(new_span_boundary)
batch=[('1', {"speech":speech,"align_start":align_start,"align_end":align_end,"text":text,"span_boundary":span_boundary})]
......@@ -422,17 +402,19 @@ mask_reconstruct=False, train_args=None):
return batch, old_span_boundary, new_span_boundary
def decode_with_model(uid, prefix, clone_uid, clone_prefix, source_language, target_language, mlm_model, processor, collate_fn, wav_path, old_str, new_str,duration_preditor_path, sid=None, decoder=False,use_teacher_forcing=False,duration_adjust=True,start_end_sp=False, train_args=None):
# fs, hop_length = mlm_model.feats_extract.fs, mlm_model.feats_extract.hop_length
fs, hop_length = train_args.feats_extract_conf['fs'], train_args.feats_extract_conf['hop_length']
batch,old_span_boundary,new_span_boundary = prepare_features(uid,prefix, clone_uid, clone_prefix, source_language, target_language, mlm_model,processor,wav_path,old_str,new_str,duration_preditor_path, sid,duration_adjust=duration_adjust,start_end_sp=start_end_sp, train_args=train_args)
feats = pickle.load(open('tmp/tmp_pkl.'+str(uid), 'rb'))
tmp = feats['speech'][0]
# print('feats end')
# wav_len * 80
# set_all_random_seed(9999)
if 'text_masked_position' in feats.keys():
feats.pop('text_masked_position')
for k, v in feats.items():
feats[k] = paddle.to_tensor(v)
rtn = mlm_model.inference(**feats,span_boundary=new_span_boundary,use_teacher_forcing=use_teacher_forcing)
......@@ -446,612 +428,24 @@ def decode_with_model(uid, prefix, clone_uid, clone_prefix, source_language, tar
else:
output_feat = paddle.concat([output[0].squeeze(0)]+ output[1:-1]+[output[-1].squeeze(0)], axis=0).cpu()
# wav_org, rate = soundfile.read(
# wav_path, always_2d=False)
wav_org, rate = librosa.load(wav_path, sr=train_args.feats_extract_conf['fs'])
origin_speech = paddle.to_tensor(np.array(wav_org,dtype=np.float32)).unsqueeze(0)
speech_lengths = paddle.to_tensor(len(wav_org)).unsqueeze(0)
# input_feat, feats_lengths = mlm_model.feats_extract(origin_speech, speech_lengths)
# return wav_org, input_feat.squeeze(), output_feat, old_span_boundary, new_span_boundary, fs, hop_length
return wav_org, None, output_feat, old_span_boundary, new_span_boundary, fs, hop_length
class MLMCollateFn:
"""Functor class of common_collate_fn()"""
def __init__(
self,
feats_extract,
float_pad_value: Union[float, int] = 0.0,
int_pad_value: int = -32768,
not_sequence: Collection[str] = (),
mlm_prob: float=0.8,
mean_phn_span: int=8,
attention_window: int=0,
pad_speech: bool=False,
sega_emb: bool=False,
duration_collect: bool=False,
text_masking: bool=False
):
self.mlm_prob=mlm_prob
self.mean_phn_span=mean_phn_span
self.feats_extract = feats_extract
self.float_pad_value = float_pad_value
self.int_pad_value = int_pad_value
self.not_sequence = set(not_sequence)
self.attention_window=attention_window
self.pad_speech=pad_speech
self.sega_emb=sega_emb
self.duration_collect = duration_collect
self.text_masking = text_masking
def __repr__(self):
return (
f"{self.__class__}(float_pad_value={self.float_pad_value}, "
f"int_pad_value={self.float_pad_value})"
)
def __call__(
self, data: Collection[Tuple[str, Dict[str, np.ndarray]]]
) -> Tuple[List[str], Dict[str, paddle.Tensor]]:
return mlm_collate_fn(
data,
float_pad_value=self.float_pad_value,
int_pad_value=self.int_pad_value,
not_sequence=self.not_sequence,
mlm_prob=self.mlm_prob,
mean_phn_span=self.mean_phn_span,
feats_extract=self.feats_extract,
attention_window=self.attention_window,
pad_speech=self.pad_speech,
sega_emb=self.sega_emb,
duration_collect=self.duration_collect,
text_masking=self.text_masking
)
def pad_list(xs, pad_value):
"""Perform padding for the list of tensors.
Args:
xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
pad_value (float): Value for padding.
Returns:
Tensor: Padded tensor (B, Tmax, `*`).
Examples:
>>> x = [paddle.ones(4), paddle.ones(2), paddle.ones(1)]
>>> x
[tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])]
>>> pad_list(x, 0)
tensor([[1., 1., 1., 1.],
[1., 1., 0., 0.],
[1., 0., 0., 0.]])
"""
n_batch = len(xs)
max_len = max(paddle.shape(x)[0] for x in xs)
pad = paddle.full((n_batch, max_len), pad_value, dtype = xs[0].dtype)
for i in range(n_batch):
pad[i, : paddle.shape(xs[i])[0]] = xs[i]
return pad
def pad_to_longformer_att_window(text, max_len, max_tlen,attention_window):
round = max_len % attention_window
if round != 0:
max_tlen += (attention_window - round)
n_batch = paddle.shape(text)[0]
text_pad = paddle.zeros((n_batch, max_tlen, *paddle.shape(text[0])[1:]), dtype=text.dtype)
for i in range(n_batch):
text_pad[i, : paddle.shape(text[i])[0]] = text[i]
else:
text_pad = text[:, : max_tlen]
return text_pad, max_tlen
def make_pad_mask(lengths, xs=None, length_dim=-1):
print('inputs are:', lengths, xs, length_dim)
"""Make mask tensor containing indices of padded part.
Args:
lengths (LongTensor or List): Batch of lengths (B,).
xs (Tensor, optional): The reference tensor.
If set, masks will be the same shape as this tensor.
length_dim (int, optional): Dimension indicator of the above tensor.
See the example.
Returns:
Tensor: Mask tensor containing indices of padded part.
Examples:
With only lengths.
>>> lengths = [5, 3, 2]
>>> make_non_pad_mask(lengths)
masks = [[0, 0, 0, 0 ,0],
[0, 0, 0, 1, 1],
[0, 0, 1, 1, 1]]
With the reference tensor.
>>> xs = paddle.zeros((3, 2, 4))
>>> make_pad_mask(lengths, xs)
tensor([[[0, 0, 0, 0],
[0, 0, 0, 0]],
[[0, 0, 0, 1],
[0, 0, 0, 1]],
[[0, 0, 1, 1],
[0, 0, 1, 1]]], dtype=paddle.uint8)
>>> xs = paddle.zeros((3, 2, 6))
>>> make_pad_mask(lengths, xs)
tensor([[[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1]],
[[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1]],
[[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1]]], dtype=paddle.uint8)
With the reference tensor and dimension indicator.
>>> xs = paddle.zeros((3, 6, 6))
>>> make_pad_mask(lengths, xs, 1)
tensor([[[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1]],
[[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1]],
[[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1]]], dtype=paddle.uint8)
>>> make_pad_mask(lengths, xs, 2)
tensor([[[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1]],
[[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1]],
[[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1]]], dtype=paddle.uint8)
"""
if length_dim == 0:
raise ValueError("length_dim cannot be 0: {}".format(length_dim))
if not isinstance(lengths, list):
lengths = list(lengths)
print('lengths', lengths)
bs = int(len(lengths))
if xs is None:
maxlen = int(max(lengths))
else:
maxlen = paddle.shape(xs)[length_dim]
seq_range = paddle.arange(0, maxlen, dtype=paddle.int64)
seq_range_expand = paddle.expand(paddle.unsqueeze(seq_range, 0), (bs, maxlen))
seq_length_expand = paddle.unsqueeze(paddle.to_tensor(lengths), -1)
print('seq_length_expand', paddle.shape(seq_length_expand))
print('seq_range_expand', paddle.shape(seq_range_expand))
mask = seq_range_expand >= seq_length_expand
if xs is not None:
assert paddle.shape(xs)[0] == bs, (paddle.shape(xs)[0], bs)
if length_dim < 0:
length_dim = len(paddle.shape(xs)) + length_dim
# ind = (:, None, ..., None, :, , None, ..., None)
ind = tuple(
slice(None) if i in (0, length_dim) else None for i in range(len(paddle.shape(xs)))
)
print('0:', paddle.shape(mask))
print('1:', paddle.shape(mask[ind]))
print('2:', paddle.shape(xs))
mask = paddle.expand(mask[ind], paddle.shape(xs))
return mask
def make_non_pad_mask(lengths, xs=None, length_dim=-1):
"""Make mask tensor containing indices of non-padded part.
Args:
lengths (LongTensor or List): Batch of lengths (B,).
xs (Tensor, optional): The reference tensor.
If set, masks will be the same shape as this tensor.
length_dim (int, optional): Dimension indicator of the above tensor.
See the example.
Returns:
ByteTensor: mask tensor containing indices of padded part.
Examples:
With only lengths.
>>> lengths = [5, 3, 2]
>>> make_non_pad_mask(lengths)
masks = [[1, 1, 1, 1 ,1],
[1, 1, 1, 0, 0],
[1, 1, 0, 0, 0]]
With the reference tensor.
>>> xs = paddle.zeros((3, 2, 4))
>>> make_non_pad_mask(lengths, xs)
tensor([[[1, 1, 1, 1],
[1, 1, 1, 1]],
[[1, 1, 1, 0],
[1, 1, 1, 0]],
[[1, 1, 0, 0],
[1, 1, 0, 0]]], dtype=paddle.uint8)
>>> xs = paddle.zeros((3, 2, 6))
>>> make_non_pad_mask(lengths, xs)
tensor([[[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 0]],
[[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0]],
[[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0]]], dtype=paddle.uint8)
With the reference tensor and dimension indicator.
>>> xs = paddle.zeros((3, 6, 6))
>>> make_non_pad_mask(lengths, xs, 1)
tensor([[[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0]],
[[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]],
[[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]]], dtype=paddle.uint8)
>>> make_non_pad_mask(lengths, xs, 2)
tensor([[[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 0]],
[[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0]],
[[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0]]], dtype=paddle.uint8)
"""
return ~make_pad_mask(lengths, xs, length_dim)
def phones_masking(xs_pad, src_mask, align_start, align_end, align_start_lengths, mlm_prob, mean_phn_span, span_boundary=None):
bz, sent_len, _ = paddle.shape(xs_pad)
mask_num_lower = math.ceil(sent_len * mlm_prob)
masked_position = np.zeros((bz, sent_len))
y_masks = None
# y_masks = torch.ones(bz,sent_len,sent_len,device=xs_pad.device,dtype=xs_pad.dtype)
# tril_masks = torch.tril(y_masks)
if mlm_prob == 1.0:
masked_position += 1
# y_masks = tril_masks
elif mean_phn_span == 0:
# only speech
length = sent_len
mean_phn_span = min(length*mlm_prob//3, 50)
masked_phn_indices = random_spans_noise_mask(length,mlm_prob, mean_phn_span).nonzero()
masked_position[:,masked_phn_indices]=1
else:
for idx in range(bz):
if span_boundary is not None:
for s,e in zip(span_boundary[idx][::2], span_boundary[idx][1::2]):
masked_position[idx, s:e] = 1
# y_masks[idx, :, s:e] = tril_masks[idx, :, s:e]
# y_masks[idx, e:, s:e ] = 0
else:
length = align_start_lengths[idx].item()
if length<2:
continue
masked_phn_indices = random_spans_noise_mask(length,mlm_prob, mean_phn_span).nonzero()
masked_start = align_start[idx][masked_phn_indices].tolist()
masked_end = align_end[idx][masked_phn_indices].tolist()
for s,e in zip(masked_start, masked_end):
masked_position[idx, s:e] = 1
# y_masks[idx, :, s:e] = tril_masks[idx, :, s:e]
# y_masks[idx, e:, s:e ] = 0
non_eos_mask = np.array(paddle.reshape(src_mask, paddle.shape(xs_pad)[:2]).float().cpu())
masked_position = masked_position * non_eos_mask
# y_masks = src_mask & y_masks.bool()
return paddle.cast(paddle.to_tensor(masked_position), paddle.bool), y_masks
def get_segment_pos(speech_pad, text_pad, align_start, align_end, align_start_lengths,sega_emb):
bz, speech_len, _ = speech_pad.size()
text_segment_pos = paddle.zeros_like(text_pad)
speech_segment_pos = paddle.zeros((bz, speech_len),dtype=text_pad.dtype)
if not sega_emb:
return speech_segment_pos, text_segment_pos
for idx in range(bz):
align_length = align_start_lengths[idx].item()
for j in range(align_length):
s,e = align_start[idx][j].item(), align_end[idx][j].item()
speech_segment_pos[idx][s:e] = j+1
text_segment_pos[idx][j] = j+1
return speech_segment_pos, text_segment_pos
def mlm_collate_fn(
data: Collection[Tuple[str, Dict[str, np.ndarray]]],
float_pad_value: Union[float, int] = 0.0,
int_pad_value: int = -32768,
not_sequence: Collection[str] = (),
mlm_prob: float = 0.8,
mean_phn_span: int = 8,
feats_extract=None,
attention_window: int = 0,
pad_speech: bool=False,
sega_emb: bool=False,
duration_collect: bool=False,
text_masking: bool=False
) -> Tuple[List[str], Dict[str, paddle.Tensor]]:
"""Concatenate ndarray-list to an array and convert to paddle.Tensor.
Examples:
>>> from espnet2.samplers.constant_batch_sampler import ConstantBatchSampler,
>>> import espnet2.tasks.abs_task
>>> from espnet2.train.dataset import ESPnetDataset
>>> sampler = ConstantBatchSampler(...)
>>> dataset = ESPnetDataset(...)
>>> keys = next(iter(sampler)
>>> batch = [dataset[key] for key in keys]
>>> batch = common_collate_fn(batch)
>>> model(**batch)
Note that the dict-keys of batch are propagated from
that of the dataset as they are.
"""
uttids = [u for u, _ in data]
data = [d for _, d in data]
assert all(set(data[0]) == set(d) for d in data), "dict-keys mismatching"
assert all(
not k.endswith("_lengths") for k in data[0]
), f"*_lengths is reserved: {list(data[0])}"
output = {}
for key in data[0]:
# NOTE(kamo):
# Each models, which accepts these values finally, are responsible
# to repaint the pad_value to the desired value for each tasks.
if data[0][key].dtype.kind == "i":
pad_value = int_pad_value
else:
pad_value = float_pad_value
array_list = [d[key] for d in data]
# Assume the first axis is length:
# tensor_list: Batch x (Length, ...)
tensor_list = [paddle.to_tensor(a) for a in array_list]
# tensor: (Batch, Length, ...)
tensor = pad_list(tensor_list, pad_value)
output[key] = tensor
# lens: (Batch,)
if key not in not_sequence:
lens = paddle.to_tensor([d[key].shape[0] for d in data], dtype=paddle.long)
output[key + "_lengths"] = lens
f = open('tmp_var.out', 'w')
for item in [round(item, 6) for item in output["speech"][0].tolist()]:
f.write(str(item)+'\n')
feats = feats_extract.get_log_mel_fbank(np.array(output["speech"][0]))
feats = paddle.to_tensor(feats)
print('out shape', paddle.shape(feats))
feats_lengths = paddle.shape(feats)[0]
feats = paddle.unsqueeze(feats, 0)
batch_size = paddle.shape(feats)[0]
if 'text' not in output:
text=paddle.zeros_like(feats_lengths.unsqueeze(-1))-2
text_lengths=paddle.zeros_like(feats_lengths)+1
max_tlen=1
align_start=paddle.zeros_like(text)
align_end=paddle.zeros_like(text)
align_start_lengths=paddle.zeros_like(feats_lengths)
align_end_lengths=paddle.zeros_like(feats_lengths)
sega_emb=False
mean_phn_span = 0
mlm_prob = 0.15
else:
text, text_lengths = output["text"], output["text_lengths"]
align_start, align_start_lengths, align_end, align_end_lengths = output["align_start"], output["align_start_lengths"], output["align_end"], output["align_end_lengths"]
align_start = paddle.floor(feats_extract.sr*align_start/feats_extract.hop_length).int()
align_end = paddle.floor(feats_extract.sr*align_end/feats_extract.hop_length).int()
max_tlen = max(text_lengths).item()
max_slen = max(feats_lengths).item()
speech_pad = feats[:, : max_slen]
if attention_window>0 and pad_speech:
speech_pad,max_slen = pad_to_longformer_att_window(speech_pad, max_slen, max_slen, attention_window)
max_len = max_slen + max_tlen
if attention_window>0:
text_pad, max_tlen = pad_to_longformer_att_window(text, max_len, max_tlen, attention_window)
else:
text_pad = text
text_mask = make_non_pad_mask(text_lengths.tolist(), text_pad, length_dim=1).unsqueeze(-2)
if attention_window>0:
text_mask = text_mask*2
speech_mask = make_non_pad_mask(feats_lengths.tolist(), speech_pad[:,:,0], length_dim=1).unsqueeze(-2)
span_boundary = None
if 'span_boundary' in output.keys():
span_boundary = output['span_boundary']
if text_masking:
masked_position, text_masked_position,_ = phones_text_masking(
speech_pad,
speech_mask,
text_pad,
text_mask,
align_start,
align_end,
align_start_lengths,
mlm_prob,
mean_phn_span,
span_boundary)
else:
text_masked_position = np.zeros(text_pad.size())
masked_position, _ = phones_masking(
speech_pad,
speech_mask,
align_start,
align_end,
align_start_lengths,
mlm_prob,
mean_phn_span,
span_boundary)
output_dict = {}
if duration_collect and 'text' in output:
reordered_index, speech_segment_pos,text_segment_pos, durations,feats_lengths = get_segment_pos_reduce_duration(speech_pad, text_pad, align_start, align_end, align_start_lengths,sega_emb, masked_position, feats_lengths)
speech_mask = make_non_pad_mask(feats_lengths.tolist(), speech_pad[:,:reordered_index.shape[1],0], length_dim=1).unsqueeze(-2)
output_dict['durations'] = durations
output_dict['reordered_index'] = reordered_index
else:
speech_segment_pos, text_segment_pos = get_segment_pos(speech_pad, text_pad, align_start, align_end, align_start_lengths,sega_emb)
output_dict['speech'] = speech_pad
output_dict['text'] = text_pad
output_dict['masked_position'] = masked_position
output_dict['text_masked_position'] = text_masked_position
output_dict['speech_mask'] = speech_mask
output_dict['text_mask'] = text_mask
output_dict['speech_segment_pos'] = speech_segment_pos
output_dict['text_segment_pos'] = text_segment_pos
# output_dict['y_masks'] = y_masks
output_dict['speech_lengths'] = output["speech_lengths"]
output_dict['text_lengths'] = text_lengths
output = (uttids, output_dict)
# assert check_return_type(output)
return output
def build_collate_fn(
args: argparse.Namespace, train: bool, epoch=-1
):
# assert check_argument_types()
# return CommonCollateFn(float_pad_value=0.0, int_pad_value=0)
feats_extract_class = LogMelFBank
args_dic = {}
print ('type is', type(args.feats_extract_conf))
for k, v in args.feats_extract_conf.items():
if k == 'fs':
args_dic['sr'] = v
else:
args_dic[k] = v
# feats_extract = feats_extract_class(**args.feats_extract_conf)
feats_extract = feats_extract_class(**args_dic)
sega_emb = True if args.encoder_conf['input_layer'] == 'sega_mlm' else False
if args.encoder_conf['selfattention_layer_type'] == 'longformer':
attention_window = args.encoder_conf['attention_window']
pad_speech = True if 'pre_speech_layer' in args.encoder_conf and args.encoder_conf['pre_speech_layer'] >0 else False
else:
attention_window=0
pad_speech=False
if epoch==-1:
mlm_prob_factor = 1
else:
mlm_probs = [1.0, 1.0, 0.7, 0.6, 0.5]
mlm_prob_factor = 0.8 #mlm_probs[epoch // 100]
if 'duration_predictor_layers' in args.model_conf.keys() and args.model_conf['duration_predictor_layers']>0:
duration_collect=True
else:
duration_collect=False
return MLMCollateFn(feats_extract, float_pad_value=0.0, int_pad_value=0,
mlm_prob=args.model_conf['mlm_prob']*mlm_prob_factor,mean_phn_span=args.model_conf['mean_phn_span'],attention_window=attention_window,pad_speech=pad_speech,sega_emb=sega_emb,duration_collect=duration_collect)
def get_mlm_output(uid, prefix, clone_uid, clone_prefix, source_language, target_language, model_name, wav_path, old_str, new_str,duration_preditor_path, sid=None, decoder=False,use_teacher_forcing=False, dynamic_eval=(0,0),duration_adjust=True,start_end_sp=False):
mlm_model,train_args = load_model(model_name)
mlm_model.eval()
# processor = MLMTask.build_preprocess_fn(train_args, False)
processor = None
collate_fn = MLMTask.build_collate_fn(train_args, False)
# collate_fn = build_collate_fn(train_args, False)
collate_fn = None
return decode_with_model(uid,prefix, clone_uid, clone_prefix, source_language, target_language, mlm_model, processor, collate_fn, wav_path, old_str, new_str,duration_preditor_path, sid=sid, decoder=decoder, use_teacher_forcing=use_teacher_forcing,
duration_adjust=duration_adjust,start_end_sp=start_end_sp, train_args = train_args)
def prompt_decoding_fn(model_name, wav_path,full_origin_str, old_str, new_str, vocoder,duration_preditor_path,sid=None, non_autoreg=True, dynamic_eval=(0,0),duration_adjust=True):
wav_org, input_feat, output_feat, old_span_boundary, new_span_boundary, fs, hop_length = get_mlm_output(
model_name,
wav_path,
old_str,
new_str,
duration_preditor_path,
use_teacher_forcing=non_autoreg,
sid=sid,
dynamic_eval=dynamic_eval,
duration_adjust=duration_adjust,
start_end_sp=False
)
replaced_wav = vocoder(output_feat).detach().float().data.cpu().numpy()
old_time_boundary = [hop_length * x for x in old_span_boundary]
new_time_boundary = [hop_length * x for x in new_span_boundary]
new_wav = replaced_wav[new_time_boundary[0]:]
# "origin_vocoder":vocoder_origin_wav,
data_dict = {"prompt":wav_org,
"new_wav":new_wav}
return data_dict
def test_vctk(uid, clone_uid, clone_prefix, source_language, target_language, vocoder, prefix='dump/raw/dev', model_name="conformer", old_str="",new_str="",prompt_decoding=False,dynamic_eval=(0,0), task_name = None):
new_str = new_str.strip()
if clone_uid is not None and clone_prefix is not None:
if target_language == "english":
duration_preditor_path = duration_path_dict['ljspeech']
elif target_language == "chinese":
duration_preditor_path = duration_path_dict['ljspeech']
else:
assert target_language == "chinese" or target_language == "english", "duration_preditor_path is not support for this language..."
else:
duration_preditor_path = duration_path_dict['ljspeech']
duration_preditor_path = None
spemd = None
full_origin_str,wav_path = read_data(uid, prefix)
......@@ -1060,12 +454,7 @@ def test_vctk(uid, clone_uid, clone_prefix, source_language, target_language, vo
if not old_str:
old_str = full_origin_str
if not new_str:
new_str = input("input the new string:")
if prompt_decoding:
print(new_str)
return prompt_decoding_fn(model_name, wav_path,full_origin_str, old_str, new_str,vocoder,duration_preditor_path,sid=spemd,dynamic_eval=dynamic_eval)
print(full_origin_str)
results_dict, old_span = plot_mel_and_vocode_wav(uid, prefix, clone_uid, clone_prefix, source_language, target_language, model_name, wav_path,full_origin_str, old_str, new_str,vocoder,duration_preditor_path,sid=spemd)
return results_dict
......@@ -1083,4 +472,4 @@ if __name__ == "__main__":
new_str=args.new_str,
task_name=args.task_name)
sf.write('./wavs/%s' % args.output_name, data_dict['output'], samplerate=24000)
\ No newline at end of file
# exit()
ernie-sat/prompt/dev/mfa_end
浏览文件 @ c2f4709e
p243_new 1.0225 1.0525 1.0925 1.1325 1.1725 1.2625 1.3625 1.4125 1.5125 1.6225 1.6625 1.7925 1.8625 2.0025 2.0925 2.1725 2.2625 2.4325 2.4725 2.5225 2.5825 2.6125 2.6425 2.7425 2.8025 2.9025 2.9525 3.0525 3.0825 3.2125 3.4525
Prompt_003_new 0.0425 0.0925 0.1825 0.2125 0.2425 0.3225 0.3725 0.4725 0.5325 0.5625 0.6225 0.7425 0.8625 0.9725 0.9975 1.0125 1.0825 1.2625 1.3125
p299_096 0.7525 0.7925 0.8725 0.9125 0.9425 1.0325 1.0625 1.1925 1.2625 1.3225 1.3725 1.4125 1.5125 1.5425 1.6525 1.6925 1.7325 1.7625 1.8425 1.9625 2.0225 2.1825 2.3325 2.6825
p243_new 1.0225 1.0525 1.0925 1.1325 1.1725 1.2625 1.3625 1.4125 1.5125 1.6225 1.6625 1.7925 1.8625 2.0025 2.0925 2.1725 2.2625 2.4325 2.4725 2.5225 2.5825 2.6125 2.6425 2.7425 2.8025 2.9025 2.9525 3.0525 3.0825 3.2125 3.4525
ernie-sat/prompt/dev/mfa_start
浏览文件 @ c2f4709e
Prompt_003_new 0.0125 0.0425 0.0925 0.1825 0.2125 0.2425 0.3225 0.3725 0.4725 0.5325 0.5625 0.6225 0.7425 0.8625 0.9725 0.9975 1.0125 1.0825 1.2625
p243_new 0.0125 1.0225 1.0525 1.0925 1.1325 1.1725 1.2625 1.3625 1.4125 1.5125 1.6225 1.6625 1.7925 1.8625 2.0025 2.0925 2.1725 2.2625 2.4325 2.4725 2.5225 2.5825 2.6125 2.6425 2.7425 2.8025 2.9025 2.9525 3.0525 3.0825 3.2125
Prompt_003_new 0.0125 0.0425 0.0925 0.1825 0.2125 0.2425 0.3225 0.3725 0.4725 0.5325 0.5625 0.6225 0.7425 0.8625 0.9725 0.9975 1.0125 1.0825 1.2625
p299_096 0.0125 0.7525 0.7925 0.8725 0.9125 0.9425 1.0325 1.0625 1.1925 1.2625 1.3225 1.3725 1.4125 1.5125 1.5425 1.6525 1.6925 1.7325 1.7625 1.8425 1.9625 2.0225 2.1825 2.3325
ernie-sat/prompt/dev/mfa_text
浏览文件 @ c2f4709e
p243_new sp F AO1 R DH AE1 T R IY1 Z AH0 N sp K AH1 V ER0 SH UH1 D N AA1 T B IY1 G IH1 V AH0 N sp
Prompt_003_new DH IH1 S W AA1 Z N AA1 T DH AH0 SH OW1 F AO1 R M IY1 sp
p299_096 sp W IY1 AA1 R T R AY1 NG T UW1 AH0 S T AE1 B L IH0 SH AH0 D EY1 T sp
p243_new sp F AO1 R DH AE1 T R IY1 Z AH0 N sp K AH1 V ER0 SH UH1 D N AA1 T B IY1 G IH1 V AH0 N sp
ernie-sat/prompt/dev/mfa_wav.scp
浏览文件 @ c2f4709e
Prompt_003_new ../../prompt_wav/this_was_not_the_show_for_me.wav
p243_new ../../prompt_wav/p243_313.wav
Prompt_003_new ../../prompt_wav/this_was_not_the_show_for_me.wav
p299_096 ../../prompt_wav/p299_096.wav
ernie-sat/prompt/dev/text
浏览文件 @ c2f4709e
Prompt_003_new This was not the show for me.
p243_new For that reason cover should not be given.
Prompt_003_new This was not the show for me.
p299_096 We are trying to establish a date.
ernie-sat/prompt/dev/wav.scp
浏览文件 @ c2f4709e
p243_new ../../prompt_wav/p243_313.wav
Prompt_003_new ../../prompt_wav/this_was_not_the_show_for_me.wav
p299_096 ../../prompt_wav/p299_096.wav
p243_new ../../prompt_wav/p243_313.wav
ernie-sat/run_clone_en_to_zh.sh
浏览文件 @ c2f4709e
# en --> zh 的 clone
python sedit_inference_0520.py \
# en --> zh 的 语音合成
# 根据Prompt_003_new对应的语音: This was not the show for me. 来合成: '今天天气很好'
python inference.py \
--task_name cross-lingual_clone \
--model_name paddle_checkpoint_ench \
--uid Prompt_003_new \
--new_str '今天天气很好' \
--prefix ./prompt/dev/ \
--clone_prefix ./prompt/dev_aishell3/ \
--clone_uid SSB07510054 \
--source_language english \
--target_language chinese \
--output_name task_cross_lingual_pred.wav \
--output_name pred_zh.wav \
--voc pwgan_aishell3 \
--voc_config download/pwg_aishell3_ckpt_0.5/default.yaml \
--voc_ckpt download/pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
......
ernie-sat/run_gen_en.sh
浏览文件 @ c2f4709e
# 纯英文的语音合成
# python sedit_inference_0518.py \
# --task_name synthesize \
# --model_name paddle_checkpoint_en \
# --uid p323_083 \
# --new_str 'I enjoy my life.' \
# --prefix ./prompt/dev/ \
# --source_language english \
# --target_language english \
# --output_name pred.wav \
# --voc pwgan_aishell3 \
# --voc_config download/pwg_aishell3_ckpt_0.5/default.yaml \
# --voc_ckpt download/pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
# --voc_stat download/pwg_aishell3_ckpt_0.5/feats_stats.npy \
# --am fastspeech2_ljspeech \
# --am_config download/fastspeech2_nosil_ljspeech_ckpt_0.5/default.yaml \
# --am_ckpt download/fastspeech2_nosil_ljspeech_ckpt_0.5/snapshot_iter_100000.pdz \
# --am_stat download/fastspeech2_nosil_ljspeech_ckpt_0.5/speech_stats.npy \
# --phones_dict download/fastspeech2_nosil_ljspeech_ckpt_0.5/phone_id_map.txt
# 根据p299_096对应的语音: This was not the show for me. 来合成: 'I enjoy my life.'
# 纯英文的语音合成
python sedit_inference_0520.py \
python inference.py \
--task_name synthesize \
--model_name paddle_checkpoint_en \
--uid p299_096 \
......@@ -28,7 +9,7 @@ python sedit_inference_0520.py \
--prefix ./prompt/dev/ \
--source_language english \
--target_language english \
--output_name task_synthesize_pred.wav \
--output_name pred.wav \
--voc pwgan_aishell3 \
--voc_config download/pwg_aishell3_ckpt_0.5/default.yaml \
--voc_ckpt download/pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
......
ernie-sat/run_sedit_en.sh
浏览文件 @ c2f4709e
# 纯英文的语音编辑
python sedit_inference_0520.py \
# 将p243_new对应的原始语音: For that reason cover should not be given. 编辑成'for that reason cover is impossible to be given.'对应的语音
python inference.py \
--task_name edit \
--model_name paddle_checkpoint_en \
--uid p243_new \
......@@ -7,7 +9,7 @@ python sedit_inference_0520.py \
--prefix ./prompt/dev/ \
--source_language english \
--target_language english \
--output_name task_edit_pred.wav \
--output_name pred.wav \
--voc pwgan_aishell3 \
--voc_config download/pwg_aishell3_ckpt_0.5/default.yaml \
--voc_ckpt download/pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \
......
ernie-sat/util.py → ernie-sat/utils.py
浏览文件 @ c2f4709e
......@@ -70,13 +70,15 @@ def get_voc_out(mel, target_language="chinese"):
print("current vocoder: ", args.voc)
with open(args.voc_config) as f:
voc_config = CfgNode(yaml.safe_load(f))
# print(voc_config)
voc_inference = get_voc_inference(args, voc_config)
mel = paddle.to_tensor(mel)
# print("masked_mel: ", mel.shape)
with paddle.no_grad():
wav = voc_inference(mel)
print("shepe of wav (time x n_channels):%s"%wav.shape) # (31800,1)
# print("shepe of wav (time x n_channels):%s"%wav.shape)
return np.squeeze(wav)
# dygraph
......@@ -134,6 +136,7 @@ def get_am_inference(args, am_config):
def evaluate_durations(phns, target_language="chinese", fs=24000, hop_length=300):
args = parse_args()
# args = parser.parse_args(args=[])
if args.ngpu == 0:
paddle.set_device("cpu")
elif args.ngpu > 0:
......@@ -154,6 +157,7 @@ def evaluate_durations(phns, target_language="chinese", fs=24000, hop_length=300
# acoustic model
am, am_inference, am_name, am_dataset,phn_id = get_am_inference(args, am_config)
torch_phns = phns
vocab_phones = {}
for tone, id in phn_id:
......@@ -165,17 +169,16 @@ def evaluate_durations(phns, target_language="chinese", fs=24000, hop_length=300
]
phone_ids = [vocab_phones[item] for item in phonemes]
phone_ids_new = phone_ids
phone_ids_new.append(vocab_size-1)
phone_ids_new = paddle.to_tensor(np.array(phone_ids_new, np.int64))
normalized_mel, d_outs, p_outs, e_outs = am.inference(phone_ids_new, spk_id=None, spk_emb=None)
pre_d_outs = d_outs
phoneme_durations_new = pre_d_outs * hop_length / fs
phoneme_durations_new = phoneme_durations_new.tolist()[:-1]
return phoneme_durations_new
def sentence2phns(sentence, target_language="en"):
args = parse_args()
if target_language == 'en':
......
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