From 5c8136b0970471d7fa19e0420767e6f2a00bc602 Mon Sep 17 00:00:00 2001 From: KP <109694228@qq.com> Date: Mon, 8 Nov 2021 15:33:38 +0800 Subject: [PATCH] Add speech models. (#1678) --- docs/docs_ch/get_start/mac_quickstart.md | 2 +- .../audio/asr/deepspeech2_aishell/README.md | 153 + .../audio/asr/deepspeech2_aishell/__init__.py | 0 .../assets/conf/augmentation.json | 1 + .../assets/conf/deepspeech2.yaml | 68 + .../assets/data/mean_std.json | 1 + .../deepspeech2_aishell/assets/data/vocab.txt | 4301 ++++++++++++++ .../deepspeech2_aishell/deepspeech_tester.py | 81 + .../audio/asr/deepspeech2_aishell/module.py | 92 + .../asr/deepspeech2_aishell/requirements.txt | 12 + .../asr/deepspeech2_librispeech/README.md | 153 + .../asr/deepspeech2_librispeech/__init__.py | 0 .../assets/conf/augmentation.json | 1 + .../assets/conf/deepspeech2.yaml | 68 + .../deepspeech_tester.py | 81 + .../asr/deepspeech2_librispeech/module.py | 93 + .../deepspeech2_librispeech/requirements.txt | 11 + .../audio/asr/u2_conformer_aishell/README.md | 156 + .../asr/u2_conformer_aishell/__init__.py | 0 .../assets/conf/augmentation.json | 1 + .../assets/conf/conformer.yaml | 102 + .../assets/data/mean_std.json | 1 + .../assets/data/vocab.txt | 4233 ++++++++++++++ .../audio/asr/u2_conformer_aishell/module.py | 73 + .../asr/u2_conformer_aishell/requirements.txt | 12 + .../u2_conformer_tester.py | 80 + .../asr/u2_conformer_librispeech/README.md | 156 + .../asr/u2_conformer_librispeech/__init__.py | 0 .../assets/conf/augmentation.json | 1 + .../assets/conf/conformer.yaml | 116 + .../assets/data/bpe_unigram_5000.model | Bin 0 -> 325121 bytes .../assets/data/bpe_unigram_5000.vocab | 5000 ++++++++++++++++ .../assets/data/mean_std.json | 1 + .../assets/data/vocab.txt | 5002 +++++++++++++++++ .../asr/u2_conformer_librispeech/module.py | 74 + .../u2_conformer_librispeech/requirements.txt | 12 + .../u2_conformer_tester.py | 80 + .../PANNs/cnn10/README.md | 141 +- .../PANNs/cnn14/README.md | 141 +- .../audio_classification/PANNs/cnn6/README.md | 137 +- modules/audio/tts/fastspeech2_baker/README.md | 156 + .../audio/tts/fastspeech2_baker/__init__.py | 0 .../default.yaml | 104 + .../phone_id_map.txt | 268 + .../pwg_baker_ckpt_0.4/pwg_default.yaml | 128 + modules/audio/tts/fastspeech2_baker/module.py | 125 + .../tts/fastspeech2_baker/requirements.txt | 1 + .../audio/tts/fastspeech2_ljspeech/README.md | 156 + .../tts/fastspeech2_ljspeech/__init__.py | 0 .../default.yaml | 104 + .../phone_id_map.txt | 80 + .../pwg_ljspeech_ckpt_0.5/pwg_default.yaml | 119 + .../audio/tts/fastspeech2_ljspeech/module.py | 130 + .../tts/fastspeech2_ljspeech/requirements.txt | 1 + 54 files changed, 21827 insertions(+), 182 deletions(-) create mode 100644 modules/audio/asr/deepspeech2_aishell/README.md create mode 100644 modules/audio/asr/deepspeech2_aishell/__init__.py create mode 100644 modules/audio/asr/deepspeech2_aishell/assets/conf/augmentation.json create mode 100644 modules/audio/asr/deepspeech2_aishell/assets/conf/deepspeech2.yaml create mode 100644 modules/audio/asr/deepspeech2_aishell/assets/data/mean_std.json create mode 100644 modules/audio/asr/deepspeech2_aishell/assets/data/vocab.txt create mode 100644 modules/audio/asr/deepspeech2_aishell/deepspeech_tester.py create mode 100644 modules/audio/asr/deepspeech2_aishell/module.py create mode 100644 modules/audio/asr/deepspeech2_aishell/requirements.txt create mode 100644 modules/audio/asr/deepspeech2_librispeech/README.md create mode 100644 modules/audio/asr/deepspeech2_librispeech/__init__.py create mode 100644 modules/audio/asr/deepspeech2_librispeech/assets/conf/augmentation.json create mode 100644 modules/audio/asr/deepspeech2_librispeech/assets/conf/deepspeech2.yaml create mode 100644 modules/audio/asr/deepspeech2_librispeech/deepspeech_tester.py create mode 100644 modules/audio/asr/deepspeech2_librispeech/module.py create mode 100644 modules/audio/asr/deepspeech2_librispeech/requirements.txt create mode 100644 modules/audio/asr/u2_conformer_aishell/README.md create mode 100644 modules/audio/asr/u2_conformer_aishell/__init__.py create mode 100644 modules/audio/asr/u2_conformer_aishell/assets/conf/augmentation.json create mode 100644 modules/audio/asr/u2_conformer_aishell/assets/conf/conformer.yaml create mode 100644 modules/audio/asr/u2_conformer_aishell/assets/data/mean_std.json create mode 100644 modules/audio/asr/u2_conformer_aishell/assets/data/vocab.txt create mode 100644 modules/audio/asr/u2_conformer_aishell/module.py create mode 100644 modules/audio/asr/u2_conformer_aishell/requirements.txt create mode 100644 modules/audio/asr/u2_conformer_aishell/u2_conformer_tester.py create mode 100644 modules/audio/asr/u2_conformer_librispeech/README.md create mode 100644 modules/audio/asr/u2_conformer_librispeech/__init__.py create mode 100644 modules/audio/asr/u2_conformer_librispeech/assets/conf/augmentation.json create mode 100644 modules/audio/asr/u2_conformer_librispeech/assets/conf/conformer.yaml create mode 100644 modules/audio/asr/u2_conformer_librispeech/assets/data/bpe_unigram_5000.model create mode 100644 modules/audio/asr/u2_conformer_librispeech/assets/data/bpe_unigram_5000.vocab create mode 100644 modules/audio/asr/u2_conformer_librispeech/assets/data/mean_std.json create mode 100644 modules/audio/asr/u2_conformer_librispeech/assets/data/vocab.txt create mode 100644 modules/audio/asr/u2_conformer_librispeech/module.py create mode 100644 modules/audio/asr/u2_conformer_librispeech/requirements.txt create mode 100644 modules/audio/asr/u2_conformer_librispeech/u2_conformer_tester.py create mode 100644 modules/audio/tts/fastspeech2_baker/README.md create mode 100644 modules/audio/tts/fastspeech2_baker/__init__.py create mode 100644 modules/audio/tts/fastspeech2_baker/assets/fastspeech2_nosil_baker_ckpt_0.4/default.yaml create mode 100644 modules/audio/tts/fastspeech2_baker/assets/fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt create mode 100644 modules/audio/tts/fastspeech2_baker/assets/pwg_baker_ckpt_0.4/pwg_default.yaml create mode 100644 modules/audio/tts/fastspeech2_baker/module.py create mode 100644 modules/audio/tts/fastspeech2_baker/requirements.txt create mode 100644 modules/audio/tts/fastspeech2_ljspeech/README.md create mode 100644 modules/audio/tts/fastspeech2_ljspeech/__init__.py create mode 100644 modules/audio/tts/fastspeech2_ljspeech/assets/fastspeech2_nosil_ljspeech_ckpt_0.5/default.yaml create mode 100644 modules/audio/tts/fastspeech2_ljspeech/assets/fastspeech2_nosil_ljspeech_ckpt_0.5/phone_id_map.txt create mode 100644 modules/audio/tts/fastspeech2_ljspeech/assets/pwg_ljspeech_ckpt_0.5/pwg_default.yaml create mode 100644 modules/audio/tts/fastspeech2_ljspeech/module.py create mode 100644 modules/audio/tts/fastspeech2_ljspeech/requirements.txt diff --git a/docs/docs_ch/get_start/mac_quickstart.md b/docs/docs_ch/get_start/mac_quickstart.md index ba765fdf..f49160d1 100755 --- a/docs/docs_ch/get_start/mac_quickstart.md +++ b/docs/docs_ch/get_start/mac_quickstart.md @@ -192,7 +192,7 @@ - output image ## 第6步：飞桨预训练模型探索之旅 -- 恭喜你，到这里PaddleHub在windows环境下的安装和入门案例就全部完成了，快快开启你更多的深度学习模型探索之旅吧。[【更多模型探索，跳转飞桨官网】](https://www.paddlepaddle.org.cn/hublist) +- 恭喜你，到这里PaddleHub在mac环境下的安装和入门案例就全部完成了，快快开启你更多的深度学习模型探索之旅吧。[【更多模型探索，跳转飞桨官网】](https://www.paddlepaddle.org.cn/hublist) diff --git a/modules/audio/asr/deepspeech2_aishell/README.md b/modules/audio/asr/deepspeech2_aishell/README.md new file mode 100644 index 00000000..a75ba672 --- /dev/null +++ b/modules/audio/asr/deepspeech2_aishell/README.md @@ -0,0 +1,153 @@ +# deepspeech2_aishell + +|模型名称|deepspeech2_aishell| +| :--- | :---: | +|类别|语音-语音识别| +|网络|DeepSpeech2| +|数据集|AISHELL-1| +|是否支持Fine-tuning|否| +|模型大小|306MB| +|最新更新日期|2021-10-20| +|数据指标|中文CER 0.065| + +## 一、模型基本信息 + +### 模型介绍 + +DeepSpeech2是百度于2015年提出的适用于英文和中文的end-to-end语音识别模型。deepspeech2_aishell使用了DeepSpeech2离线模型的结构，模型主要由2层卷积网络和3层GRU组成，并在中文普通话开源语音数据集[AISHELL-1](http://www.aishelltech.com/kysjcp)进行了预训练，该模型在其测试集上的CER指标是0.065。 + + +

+
+

+ +更多详情请参考[Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://arxiv.org/abs/1512.02595) + +## 二、安装 + +- ### 1、系统依赖 + + - libsndfile, swig >= 3.0 + - Linux + ```shell + $ sudo apt-get install libsndfile swig + or + $ sudo yum install libsndfile swig + ``` + - MacOs + ``` + $ brew install libsndfile swig + ``` + +- ### 2、环境依赖 + - swig_decoder: + ``` + git clone https://github.com/PaddlePaddle/DeepSpeech.git && cd DeepSpeech && git reset --hard b53171694e7b87abe7ea96870b2f4d8e0e2b1485 && cd deepspeech/decoders/ctcdecoder/swig && sh setup.sh + ``` + + - paddlepaddle >= 2.1.0 + + - paddlehub >= 2.1.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst) + +- ### 3、安装 + + - ```shell + $ hub install deepspeech2_aishell + ``` + - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md) + | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md) + + +## 三、模型API预测 + +- ### 1、预测代码示例 + + ```python + import paddlehub as hub + + # 采样率为16k，格式为wav的中文语音音频 + wav_file = '/PATH/TO/AUDIO' + + model = hub.Module( + name='deepspeech2_aishell', + version='1.0.0') + text = model.speech_recognize(wav_file) + + print(text) + ``` + +- ### 2、API + - ```python + def check_audio(audio_file) + ``` + - 检查输入音频格式和采样率是否满足为16000 + + - **参数** + + - `audio_file`：本地音频文件(*.wav)的路径，如`/path/to/input.wav` + + - ```python + def speech_recognize( + audio_file, + device='cpu', + ) + ``` + - 将输入的音频识别成文字 + + - **参数** + + - `audio_file`：本地音频文件(*.wav)的路径，如`/path/to/input.wav` + - `device`：预测时使用的设备，默认为`cpu`，如需使用gpu预测，请设置为`gpu`。 + + - **返回** + + - `text`：str类型，返回输入音频的识别文字结果。 + + +## 四、服务部署 + +- PaddleHub Serving可以部署一个在线的语音识别服务。 + +- ### 第一步：启动PaddleHub Serving + + - ```shell + $ hub serving start -m deepspeech2_aishell + ``` + + - 这样就完成了一个语音识别服务化API的部署，默认端口号为8866。 + + - **NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA_VISIBLE_DEVICES环境变量，否则不用设置。 + +- ### 第二步：发送预测请求 + + - 配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果 + + - ```python + import requests + import json + + # 需要识别的音频的存放路径，确保部署服务的机器可访问 + file = '/path/to/input.wav' + + # 以key的方式指定text传入预测方法的时的参数，此例中为"audio_file" + data = {"audio_file": file} + + # 发送post请求，content-type类型应指定json方式，url中的ip地址需改为对应机器的ip + url = "http://127.0.0.1:8866/predict/deepspeech2_aishell" + + # 指定post请求的headers为application/json方式 + headers = {"Content-Type": "application/json"} + + r = requests.post(url=url, headers=headers, data=json.dumps(data)) + print(r.json()) + ``` + +## 五、更新历史 + +* 1.0.0 + + 初始发布 + + ```shell + $ hub install deepspeech2_aishell + ``` diff --git a/modules/audio/asr/deepspeech2_aishell/__init__.py b/modules/audio/asr/deepspeech2_aishell/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/modules/audio/asr/deepspeech2_aishell/assets/conf/augmentation.json b/modules/audio/asr/deepspeech2_aishell/assets/conf/augmentation.json new file mode 100644 index 00000000..0967ef42 --- /dev/null +++ b/modules/audio/asr/deepspeech2_aishell/assets/conf/augmentation.json @@ -0,0 +1 @@ +{} diff --git a/modules/audio/asr/deepspeech2_aishell/assets/conf/deepspeech2.yaml b/modules/audio/asr/deepspeech2_aishell/assets/conf/deepspeech2.yaml new file mode 100644 index 00000000..ecbe9123 --- /dev/null +++ b/modules/audio/asr/deepspeech2_aishell/assets/conf/deepspeech2.yaml @@ -0,0 +1,68 @@ +# https://yaml.org/type/float.html +data: + train_manifest: data/manifest.train + dev_manifest: data/manifest.dev + test_manifest: data/manifest.test + min_input_len: 0.0 + max_input_len: 27.0 # second + min_output_len: 0.0 + max_output_len: .inf + min_output_input_ratio: 0.00 + max_output_input_ratio: .inf + +collator: + batch_size: 64 # one gpu + mean_std_filepath: data/mean_std.json + unit_type: char + vocab_filepath: data/vocab.txt + augmentation_config: conf/augmentation.json + random_seed: 0 + spm_model_prefix: + spectrum_type: linear + feat_dim: + delta_delta: False + stride_ms: 10.0 + window_ms: 20.0 + n_fft: None + max_freq: None + target_sample_rate: 16000 + use_dB_normalization: True + target_dB: -20 + dither: 1.0 + keep_transcription_text: False + sortagrad: True + shuffle_method: batch_shuffle + num_workers: 2 + +model: + num_conv_layers: 2 + num_rnn_layers: 3 + rnn_layer_size: 1024 + use_gru: True + share_rnn_weights: False + blank_id: 0 + ctc_grad_norm_type: instance + +training: + n_epoch: 80 + accum_grad: 1 + lr: 2e-3 + lr_decay: 0.83 + weight_decay: 1e-06 + global_grad_clip: 3.0 + log_interval: 100 + checkpoint: + kbest_n: 50 + latest_n: 5 + +decoding: + batch_size: 128 + error_rate_type: cer + decoding_method: ctc_beam_search + lang_model_path: data/lm/zh_giga.no_cna_cmn.prune01244.klm + alpha: 1.9 + beta: 5.0 + beam_size: 300 + cutoff_prob: 0.99 + cutoff_top_n: 40 + num_proc_bsearch: 10 diff --git 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+ｋ +ｔ + diff --git a/modules/audio/asr/deepspeech2_aishell/deepspeech_tester.py b/modules/audio/asr/deepspeech2_aishell/deepspeech_tester.py new file mode 100644 index 00000000..6b1f8975 --- /dev/null +++ b/modules/audio/asr/deepspeech2_aishell/deepspeech_tester.py @@ -0,0 +1,81 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Evaluation for DeepSpeech2 model.""" +import os +import sys +from pathlib import Path + +import paddle + +from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer +from deepspeech.io.collator import SpeechCollator +from deepspeech.models.ds2 import DeepSpeech2Model +from deepspeech.utils import mp_tools +from deepspeech.utils.utility import UpdateConfig + + +class DeepSpeech2Tester: + def __init__(self, config): + self.config = config + self.collate_fn_test = SpeechCollator.from_config(config) + self._text_featurizer = TextFeaturizer(unit_type=config.collator.unit_type, vocab_filepath=None) + + def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg): + result_transcripts = self.model.decode( + audio, + audio_len, + vocab_list, + decoding_method=cfg.decoding_method, + lang_model_path=cfg.lang_model_path, + beam_alpha=cfg.alpha, + beam_beta=cfg.beta, + beam_size=cfg.beam_size, + cutoff_prob=cfg.cutoff_prob, + cutoff_top_n=cfg.cutoff_top_n, + num_processes=cfg.num_proc_bsearch) + #replace the '' with ' ' + result_transcripts = [self._text_featurizer.detokenize(sentence) for sentence in result_transcripts] + + return result_transcripts + + @mp_tools.rank_zero_only + @paddle.no_grad() + def test(self, audio_file): + self.model.eval() + cfg = self.config + collate_fn_test = self.collate_fn_test + audio, _ = collate_fn_test.process_utterance(audio_file=audio_file, transcript=" ") + audio_len = audio.shape[0] + audio = paddle.to_tensor(audio, dtype='float32') + audio_len = paddle.to_tensor(audio_len) + audio = paddle.unsqueeze(audio, axis=0) + vocab_list = collate_fn_test.vocab_list + result_transcripts = self.compute_result_transcripts(audio, audio_len, vocab_list, cfg.decoding) + return result_transcripts + + def setup_model(self): + config = self.config.clone() + with UpdateConfig(config): + config.model.feat_size = self.collate_fn_test.feature_size + config.model.dict_size = self.collate_fn_test.vocab_size + + model = DeepSpeech2Model.from_config(config.model) + self.model = model + + def resume(self, checkpoint): + """Resume from the checkpoint at checkpoints in the output + directory or load a specified checkpoint. + """ + model_dict = paddle.load(checkpoint) + self.model.set_state_dict(model_dict) diff --git a/modules/audio/asr/deepspeech2_aishell/module.py b/modules/audio/asr/deepspeech2_aishell/module.py new file mode 100644 index 00000000..3e18e4b0 --- /dev/null +++ b/modules/audio/asr/deepspeech2_aishell/module.py @@ -0,0 +1,92 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from pathlib import Path +import sys + +import numpy as np +from paddlehub.env import MODULE_HOME +from paddlehub.module.module import moduleinfo, serving +from paddlehub.utils.log import logger +from paddle.utils.download import get_path_from_url + +try: + import swig_decoders +except ModuleNotFoundError as e: + logger.error(e) + logger.info('The module requires additional dependencies: swig_decoders. ' + 'please install via:\n\'git clone https://github.com/PaddlePaddle/DeepSpeech.git ' + '&& cd DeepSpeech && git reset --hard b53171694e7b87abe7ea96870b2f4d8e0e2b1485 ' + '&& cd deepspeech/decoders/ctcdecoder/swig && sh setup.sh\'') + sys.exit(1) + +import paddle +import soundfile as sf + +# TODO: Remove system path when deepspeech can be installed via pip. +sys.path.append(os.path.join(MODULE_HOME, 'deepspeech2_aishell')) +from deepspeech.exps.deepspeech2.config import get_cfg_defaults +from deepspeech.utils.utility import UpdateConfig +from .deepspeech_tester import DeepSpeech2Tester + +LM_URL = 'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm' +LM_MD5 = '29e02312deb2e59b3c8686c7966d4fe3' + + +@moduleinfo(name="deepspeech2_aishell", version="1.0.0", summary="", author="Baidu", author_email="", type="audio/asr") +class DeepSpeech2(paddle.nn.Layer): + def __init__(self): + super(DeepSpeech2, self).__init__() + + # resource + res_dir = os.path.join(MODULE_HOME, 'deepspeech2_aishell', 'assets') + conf_file = os.path.join(res_dir, 'conf/deepspeech2.yaml') + checkpoint = os.path.join(res_dir, 'checkpoints/avg_1.pdparams') + # Download LM manually cause its large size. + lm_path = os.path.join(res_dir, 'data', 'lm') + lm_file = os.path.join(lm_path, LM_URL.split('/')[-1]) + if not os.path.isfile(lm_file): + logger.info(f'Downloading lm from {LM_URL}.') + get_path_from_url(url=LM_URL, root_dir=lm_path, md5sum=LM_MD5) + + # config + self.model_type = 'offline' + self.config = get_cfg_defaults(self.model_type) + self.config.merge_from_file(conf_file) + + # TODO: Remove path updating snippet. + with UpdateConfig(self.config): + self.config.collator.mean_std_filepath = os.path.join(res_dir, self.config.collator.mean_std_filepath) + self.config.collator.vocab_filepath = os.path.join(res_dir, self.config.collator.vocab_filepath) + self.config.collator.augmentation_config = os.path.join(res_dir, self.config.collator.augmentation_config) + self.config.decoding.lang_model_path = os.path.join(res_dir, self.config.decoding.lang_model_path) + + # model + self.tester = DeepSpeech2Tester(self.config) + self.tester.setup_model() + self.tester.resume(checkpoint) + + @staticmethod + def check_audio(audio_file): + sig, sample_rate = sf.read(audio_file) + assert sample_rate == 16000, 'Excepting sample rate of input audio is 16000, but got {}'.format(sample_rate) + + @serving + def speech_recognize(self, audio_file, device='cpu'): + assert os.path.isfile(audio_file), 'File not exists: {}'.format(audio_file) + self.check_audio(audio_file) + + paddle.set_device(device) + return self.tester.test(audio_file)[0] diff --git a/modules/audio/asr/deepspeech2_aishell/requirements.txt b/modules/audio/asr/deepspeech2_aishell/requirements.txt new file mode 100644 index 00000000..e6f929d0 --- /dev/null +++ b/modules/audio/asr/deepspeech2_aishell/requirements.txt @@ -0,0 +1,12 @@ +# system level: libsnd swig +loguru +yacs +jsonlines +scipy==1.2.1 +sentencepiece +resampy==0.2.2 +SoundFile==0.9.0.post1 +soxbindings +kaldiio +typeguard +editdistance diff --git a/modules/audio/asr/deepspeech2_librispeech/README.md b/modules/audio/asr/deepspeech2_librispeech/README.md new file mode 100644 index 00000000..a7d4aee0 --- /dev/null +++ b/modules/audio/asr/deepspeech2_librispeech/README.md @@ -0,0 +1,153 @@ +# deepspeech2_librispeech + +|模型名称|deepspeech2_librispeech| +| :--- | :---: | +|类别|语音-语音识别| +|网络|DeepSpeech2| +|数据集|LibriSpeech| +|是否支持Fine-tuning|否| +|模型大小|518MB| +|最新更新日期|2021-10-20| +|数据指标|英文WER 0.072| + +## 一、模型基本信息 + +### 模型介绍 + +DeepSpeech2是百度于2015年提出的适用于英文和中文的end-to-end语音识别模型。deepspeech2_librispeech使用了DeepSpeech2离线模型的结构，模型主要由2层卷积网络和3层GRU组成，并在英文开源语音数据集[LibriSpeech ASR corpus](http://www.openslr.org/12/)进行了预训练，该模型在其测试集上的WER指标是0.072。 + + +

+
+

+ +更多详情请参考[Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://arxiv.org/abs/1512.02595) + +## 二、安装 + +- ### 1、系统依赖 + + - libsndfile, swig >= 3.0 + - Linux + ```shell + $ sudo apt-get install libsndfile swig + or + $ sudo yum install libsndfile swig + ``` + - MacOs + ``` + $ brew install libsndfile swig + ``` + +- ### 2、环境依赖 + - swig_decoder: + ``` + git clone https://github.com/paddlepaddle/deepspeech && cd DeepSpeech && git reset --hard b53171694e7b87abe7ea96870b2f4d8e0e2b1485 && cd deepspeech/decoders/ctcdecoder/swig && sh setup.sh + ``` + + - paddlepaddle >= 2.1.0 + + - paddlehub >= 2.1.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst) + +- ### 3、安装 + + - ```shell + $ hub install deepspeech2_librispeech + ``` + - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md) + | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md) + + +## 三、模型API预测 + +- ### 1、预测代码示例 + + ```python + import paddlehub as hub + + # 采样率为16k，格式为wav的英文语音音频 + wav_file = '/PATH/TO/AUDIO' + + model = hub.Module( + name='deepspeech2_librispeech', + version='1.0.0') + text = model.speech_recognize(wav_file) + + print(text) + ``` + +- ### 2、API + - ```python + def check_audio(audio_file) + ``` + - 检查输入音频格式和采样率是否满足为16000 + + - **参数** + + - `audio_file`：本地音频文件(*.wav)的路径，如`/path/to/input.wav` + + - ```python + def speech_recognize( + audio_file, + device='cpu', + ) + ``` + - 将输入的音频识别成文字 + + - **参数** + + - `audio_file`：本地音频文件(*.wav)的路径，如`/path/to/input.wav` + - `device`：预测时使用的设备，默认为`cpu`，如需使用gpu预测，请设置为`gpu`。 + + - **返回** + + - `text`：str类型，返回输入音频的识别文字结果。 + + +## 四、服务部署 + +- PaddleHub Serving可以部署一个在线的语音识别服务。 + +- ### 第一步：启动PaddleHub Serving + + - ```shell + $ hub serving start -m deepspeech2_librispeech + ``` + + - 这样就完成了一个语音识别服务化API的部署，默认端口号为8866。 + + - **NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA_VISIBLE_DEVICES环境变量，否则不用设置。 + +- ### 第二步：发送预测请求 + + - 配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果 + + - ```python + import requests + import json + + # 需要识别的音频的存放路径，确保部署服务的机器可访问 + file = '/path/to/input.wav' + + # 以key的方式指定text传入预测方法的时的参数，此例中为"audio_file" + data = {"audio_file": file} + + # 发送post请求，content-type类型应指定json方式，url中的ip地址需改为对应机器的ip + url = "http://127.0.0.1:8866/predict/deepspeech2_librispeech" + + # 指定post请求的headers为application/json方式 + headers = {"Content-Type": "application/json"} + + r = requests.post(url=url, headers=headers, data=json.dumps(data)) + print(r.json()) + ``` + +## 五、更新历史 + +* 1.0.0 + + 初始发布 + + ```shell + $ hub install deepspeech2_librispeech + ``` diff --git a/modules/audio/asr/deepspeech2_librispeech/__init__.py b/modules/audio/asr/deepspeech2_librispeech/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/modules/audio/asr/deepspeech2_librispeech/assets/conf/augmentation.json b/modules/audio/asr/deepspeech2_librispeech/assets/conf/augmentation.json new file mode 100644 index 00000000..0967ef42 --- /dev/null +++ b/modules/audio/asr/deepspeech2_librispeech/assets/conf/augmentation.json @@ -0,0 +1 @@ +{} diff --git a/modules/audio/asr/deepspeech2_librispeech/assets/conf/deepspeech2.yaml b/modules/audio/asr/deepspeech2_librispeech/assets/conf/deepspeech2.yaml new file mode 100644 index 00000000..c5c2e466 --- /dev/null +++ b/modules/audio/asr/deepspeech2_librispeech/assets/conf/deepspeech2.yaml @@ -0,0 +1,68 @@ +# https://yaml.org/type/float.html +data: + train_manifest: data/manifest.train + dev_manifest: data/manifest.dev-clean + test_manifest: data/manifest.test-clean + min_input_len: 0.0 + max_input_len: 30.0 # second + min_output_len: 0.0 + max_output_len: .inf + min_output_input_ratio: 0.00 + max_output_input_ratio: .inf + +collator: + batch_size: 20 + mean_std_filepath: data/mean_std.json + unit_type: char + vocab_filepath: data/vocab.txt + augmentation_config: conf/augmentation.json + random_seed: 0 + spm_model_prefix: + spectrum_type: linear + target_sample_rate: 16000 + max_freq: None + n_fft: None + stride_ms: 10.0 + window_ms: 20.0 + delta_delta: False + dither: 1.0 + use_dB_normalization: True + target_dB: -20 + random_seed: 0 + keep_transcription_text: False + sortagrad: True + shuffle_method: batch_shuffle + num_workers: 2 + +model: + num_conv_layers: 2 + num_rnn_layers: 3 + rnn_layer_size: 2048 + use_gru: False + share_rnn_weights: True + blank_id: 0 + ctc_grad_norm_type: instance + +training: + n_epoch: 50 + accum_grad: 1 + lr: 1e-3 + lr_decay: 0.83 + weight_decay: 1e-06 + global_grad_clip: 5.0 + log_interval: 100 + checkpoint: + kbest_n: 50 + latest_n: 5 + +decoding: + batch_size: 128 + error_rate_type: wer + decoding_method: ctc_beam_search + lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm + alpha: 1.9 + beta: 0.3 + beam_size: 500 + cutoff_prob: 1.0 + cutoff_top_n: 40 + num_proc_bsearch: 8 diff --git a/modules/audio/asr/deepspeech2_librispeech/deepspeech_tester.py b/modules/audio/asr/deepspeech2_librispeech/deepspeech_tester.py new file mode 100644 index 00000000..6b1f8975 --- /dev/null +++ b/modules/audio/asr/deepspeech2_librispeech/deepspeech_tester.py @@ -0,0 +1,81 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Evaluation for DeepSpeech2 model.""" +import os +import sys +from pathlib import Path + +import paddle + +from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer +from deepspeech.io.collator import SpeechCollator +from deepspeech.models.ds2 import DeepSpeech2Model +from deepspeech.utils import mp_tools +from deepspeech.utils.utility import UpdateConfig + + +class DeepSpeech2Tester: + def __init__(self, config): + self.config = config + self.collate_fn_test = SpeechCollator.from_config(config) + self._text_featurizer = TextFeaturizer(unit_type=config.collator.unit_type, vocab_filepath=None) + + def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg): + result_transcripts = self.model.decode( + audio, + audio_len, + vocab_list, + decoding_method=cfg.decoding_method, + lang_model_path=cfg.lang_model_path, + beam_alpha=cfg.alpha, + beam_beta=cfg.beta, + beam_size=cfg.beam_size, + cutoff_prob=cfg.cutoff_prob, + cutoff_top_n=cfg.cutoff_top_n, + num_processes=cfg.num_proc_bsearch) + #replace the '' with ' ' + result_transcripts = [self._text_featurizer.detokenize(sentence) for sentence in result_transcripts] + + return result_transcripts + + @mp_tools.rank_zero_only + @paddle.no_grad() + def test(self, audio_file): + self.model.eval() + cfg = self.config + collate_fn_test = self.collate_fn_test + audio, _ = collate_fn_test.process_utterance(audio_file=audio_file, transcript=" ") + audio_len = audio.shape[0] + audio = paddle.to_tensor(audio, dtype='float32') + audio_len = paddle.to_tensor(audio_len) + audio = paddle.unsqueeze(audio, axis=0) + vocab_list = collate_fn_test.vocab_list + result_transcripts = self.compute_result_transcripts(audio, audio_len, vocab_list, cfg.decoding) + return result_transcripts + + def setup_model(self): + config = self.config.clone() + with UpdateConfig(config): + config.model.feat_size = self.collate_fn_test.feature_size + config.model.dict_size = self.collate_fn_test.vocab_size + + model = DeepSpeech2Model.from_config(config.model) + self.model = model + + def resume(self, checkpoint): + """Resume from the checkpoint at checkpoints in the output + directory or load a specified checkpoint. + """ + model_dict = paddle.load(checkpoint) + self.model.set_state_dict(model_dict) diff --git a/modules/audio/asr/deepspeech2_librispeech/module.py b/modules/audio/asr/deepspeech2_librispeech/module.py new file mode 100644 index 00000000..c05d484f --- /dev/null +++ b/modules/audio/asr/deepspeech2_librispeech/module.py @@ -0,0 +1,93 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from pathlib import Path +import sys + +import numpy as np +from paddlehub.env import MODULE_HOME +from paddlehub.module.module import moduleinfo, serving +from paddlehub.utils.log import logger +from paddle.utils.download import get_path_from_url + +try: + import swig_decoders +except ModuleNotFoundError as e: + logger.error(e) + logger.info('The module requires additional dependencies: swig_decoders. ' + 'please install via:\n\'git clone https://github.com/PaddlePaddle/DeepSpeech.git ' + '&& cd DeepSpeech && git reset --hard b53171694e7b87abe7ea96870b2f4d8e0e2b1485 ' + '&& cd deepspeech/decoders/ctcdecoder/swig && sh setup.sh\'') + sys.exit(1) + +import paddle +import soundfile as sf + +# TODO: Remove system path when deepspeech can be installed via pip. +sys.path.append(os.path.join(MODULE_HOME, 'deepspeech2_librispeech')) +from deepspeech.exps.deepspeech2.config import get_cfg_defaults +from deepspeech.utils.utility import UpdateConfig +from .deepspeech_tester import DeepSpeech2Tester + +LM_URL = 'https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm' +LM_MD5 = '099a601759d467cd0a8523ff939819c5' + + +@moduleinfo( + name="deepspeech2_librispeech", version="1.0.0", summary="", author="Baidu", author_email="", type="audio/asr") +class DeepSpeech2(paddle.nn.Layer): + def __init__(self): + super(DeepSpeech2, self).__init__() + + # resource + res_dir = os.path.join(MODULE_HOME, 'deepspeech2_librispeech', 'assets') + conf_file = os.path.join(res_dir, 'conf/deepspeech2.yaml') + checkpoint = os.path.join(res_dir, 'checkpoints/avg_1.pdparams') + # Download LM manually cause its large size. + lm_path = os.path.join(res_dir, 'data', 'lm') + lm_file = os.path.join(lm_path, LM_URL.split('/')[-1]) + if not os.path.isfile(lm_file): + logger.info(f'Downloading lm from {LM_URL}.') + get_path_from_url(url=LM_URL, root_dir=lm_path, md5sum=LM_MD5) + + # config + self.model_type = 'offline' + self.config = get_cfg_defaults(self.model_type) + self.config.merge_from_file(conf_file) + + # TODO: Remove path updating snippet. + with UpdateConfig(self.config): + self.config.collator.mean_std_filepath = os.path.join(res_dir, self.config.collator.mean_std_filepath) + self.config.collator.vocab_filepath = os.path.join(res_dir, self.config.collator.vocab_filepath) + self.config.collator.augmentation_config = os.path.join(res_dir, self.config.collator.augmentation_config) + self.config.decoding.lang_model_path = os.path.join(res_dir, self.config.decoding.lang_model_path) + + # model + self.tester = DeepSpeech2Tester(self.config) + self.tester.setup_model() + self.tester.resume(checkpoint) + + @staticmethod + def check_audio(audio_file): + sig, sample_rate = sf.read(audio_file) + assert sample_rate == 16000, 'Excepting sample rate of input audio is 16000, but got {}'.format(sample_rate) + + @serving + def speech_recognize(self, audio_file, device='cpu'): + assert os.path.isfile(audio_file), 'File not exists: {}'.format(audio_file) + self.check_audio(audio_file) + + paddle.set_device(device) + return self.tester.test(audio_file)[0] diff --git a/modules/audio/asr/deepspeech2_librispeech/requirements.txt b/modules/audio/asr/deepspeech2_librispeech/requirements.txt new file mode 100644 index 00000000..66d8ba6c --- /dev/null +++ b/modules/audio/asr/deepspeech2_librispeech/requirements.txt @@ -0,0 +1,11 @@ +loguru +yacs +jsonlines +scipy==1.2.1 +sentencepiece +resampy==0.2.2 +SoundFile==0.9.0.post1 +soxbindings +kaldiio +typeguard +editdistance diff --git a/modules/audio/asr/u2_conformer_aishell/README.md b/modules/audio/asr/u2_conformer_aishell/README.md new file mode 100644 index 00000000..bd0bc64f --- /dev/null +++ b/modules/audio/asr/u2_conformer_aishell/README.md @@ -0,0 +1,156 @@ +# u2_conformer_aishell + +|模型名称|u2_conformer_aishell| +| :--- | :---: | +|类别|语音-语音识别| +|网络|DeepSpeech2| +|数据集|AISHELL-1| +|是否支持Fine-tuning|否| +|模型大小|284MB| +|最新更新日期|2021-11-01| +|数据指标|中文CER 0.055| + +## 一、模型基本信息 + +### 模型介绍 + +U2 Conformer模型是一种适用于英文和中文的end-to-end语音识别模型。u2_conformer_aishell采用了conformer的encoder和transformer的decoder的模型结构，并且使用了ctc-prefix beam search的方式进行一遍打分，再利用attention decoder进行二次打分的方式进行解码来得到最终结果。 + +u2_conformer_aishell在中文普通话开源语音数据集[AISHELL-1](http://www.aishelltech.com/kysjcp)进行了预训练，该模型在其测试集上的CER指标是0.055257。 + +

+
+

+ +

+
+

+ +更多详情请参考: +- [Unified Streaming and Non-streaming Two-pass End-to-end Model for Speech Recognition](https://arxiv.org/abs/2012.05481) +- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100) + +## 二、安装 + +- ### 1、系统依赖 + + - libsndfile + - Linux + ```shell + $ sudo apt-get install libsndfile + or + $ sudo yum install libsndfile + ``` + - MacOs + ``` + $ brew install libsndfile + ``` + +- ### 2、环境依赖 + + - paddlepaddle >= 2.1.0 + + - paddlehub >= 2.1.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst) + +- ### 3、安装 + + - ```shell + $ hub install u2_conformer_aishell + ``` + - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md) + | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md) + + +## 三、模型API预测 + +- ### 1、预测代码示例 + + ```python + import paddlehub as hub + + # 采样率为16k，格式为wav的中文语音音频 + wav_file = '/PATH/TO/AUDIO' + + model = hub.Module( + name='u2_conformer_aishell', + version='1.0.0') + text = model.speech_recognize(wav_file) + + print(text) + ``` + +- ### 2、API + - ```python + def check_audio(audio_file) + ``` + - 检查输入音频格式和采样率是否满足为16000 + + - **参数** + + - `audio_file`：本地音频文件(*.wav)的路径，如`/path/to/input.wav` + + - ```python + def speech_recognize( + audio_file, + device='cpu', + ) + ``` + - 将输入的音频识别成文字 + + - **参数** + + - `audio_file`：本地音频文件(*.wav)的路径，如`/path/to/input.wav` + - `device`：预测时使用的设备，默认为`cpu`，如需使用gpu预测，请设置为`gpu`。 + + - **返回** + + - `text`：str类型，返回输入音频的识别文字结果。 + + +## 四、服务部署 + +- PaddleHub Serving可以部署一个在线的语音识别服务。 + +- ### 第一步：启动PaddleHub Serving + + - ```shell + $ hub serving start -m u2_conformer_aishell + ``` + + - 这样就完成了一个语音识别服务化API的部署，默认端口号为8866。 + + - **NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA_VISIBLE_DEVICES环境变量，否则不用设置。 + +- ### 第二步：发送预测请求 + + - 配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果 + + - ```python + import requests + import json + + # 需要识别的音频的存放路径，确保部署服务的机器可访问 + file = '/path/to/input.wav' + + # 以key的方式指定text传入预测方法的时的参数，此例中为"audio_file" + data = {"audio_file": file} + + # 发送post请求，content-type类型应指定json方式，url中的ip地址需改为对应机器的ip + url = "http://127.0.0.1:8866/predict/u2_conformer_aishell" + + # 指定post请求的headers为application/json方式 + headers = {"Content-Type": "application/json"} + + r = requests.post(url=url, headers=headers, data=json.dumps(data)) + print(r.json()) + ``` + +## 五、更新历史 + +* 1.0.0 + + 初始发布 + + ```shell + $ hub install u2_conformer_aishell + ``` diff --git a/modules/audio/asr/u2_conformer_aishell/__init__.py b/modules/audio/asr/u2_conformer_aishell/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/modules/audio/asr/u2_conformer_aishell/assets/conf/augmentation.json b/modules/audio/asr/u2_conformer_aishell/assets/conf/augmentation.json new file mode 100644 index 00000000..0967ef42 --- /dev/null +++ b/modules/audio/asr/u2_conformer_aishell/assets/conf/augmentation.json @@ -0,0 +1 @@ +{} diff --git a/modules/audio/asr/u2_conformer_aishell/assets/conf/conformer.yaml b/modules/audio/asr/u2_conformer_aishell/assets/conf/conformer.yaml new file mode 100644 index 00000000..b6925dfc --- /dev/null +++ b/modules/audio/asr/u2_conformer_aishell/assets/conf/conformer.yaml @@ -0,0 +1,102 @@ +data: + train_manifest: data/manifest.train + dev_manifest: data/manifest.dev + test_manifest: data/manifest.test + min_input_len: 0.5 + max_input_len: 20.0 # second + min_output_len: 0.0 + max_output_len: 400.0 + min_output_input_ratio: 0.05 + max_output_input_ratio: 10.0 + +collator: + vocab_filepath: data/vocab.txt + unit_type: 'char' + spm_model_prefix: '' + augmentation_config: conf/augmentation.json + batch_size: 64 + raw_wav: True # use raw_wav or kaldi feature + spectrum_type: fbank #linear, mfcc, fbank + feat_dim: 80 + delta_delta: False + dither: 1.0 + target_sample_rate: 16000 + max_freq: None + n_fft: None + stride_ms: 10.0 + window_ms: 25.0 + use_dB_normalization: False + target_dB: -20 + random_seed: 0 + keep_transcription_text: False + sortagrad: True + shuffle_method: batch_shuffle + num_workers: 2 + +decoding: + alpha: 2.5 + batch_size: 128 + beam_size: 10 + beta: 0.3 + ctc_weight: 0.0 + cutoff_prob: 1.0 + cutoff_top_n: 0 + decoding_chunk_size: -1 + decoding_method: attention + error_rate_type: cer + lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm + num_decoding_left_chunks: -1 + num_proc_bsearch: 8 + simulate_streaming: False +model: + cmvn_file: data/mean_std.json + cmvn_file_type: json + decoder: transformer + decoder_conf: + attention_heads: 4 + dropout_rate: 0.1 + linear_units: 2048 + num_blocks: 6 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.0 + src_attention_dropout_rate: 0.0 + encoder: conformer + encoder_conf: + activation_type: swish + attention_dropout_rate: 0.0 + attention_heads: 4 + cnn_module_kernel: 15 + dropout_rate: 0.1 + input_layer: conv2d + linear_units: 2048 + normalize_before: True + num_blocks: 12 + output_size: 256 + pos_enc_layer_type: rel_pos + positional_dropout_rate: 0.1 + selfattention_layer_type: rel_selfattn + use_cnn_module: True + input_dim: 0 + model_conf: + ctc_weight: 0.3 + ctc_dropoutrate: 0.0 + ctc_grad_norm_type: instance + length_normalized_loss: False + lsm_weight: 0.1 + output_dim: 0 +training: + accum_grad: 2 + global_grad_clip: 5.0 + log_interval: 100 + n_epoch: 300 + optim: adam + optim_conf: + lr: 0.002 + weight_decay: 1e-06 + scheduler: warmuplr + scheduler_conf: + lr_decay: 1.0 + warmup_steps: 25000 + checkpoint: + kbest_n: 50 + latest_n: 5 diff --git a/modules/audio/asr/u2_conformer_aishell/assets/data/mean_std.json b/modules/audio/asr/u2_conformer_aishell/assets/data/mean_std.json new file mode 100644 index 00000000..fff0005d --- /dev/null +++ b/modules/audio/asr/u2_conformer_aishell/assets/data/mean_std.json @@ -0,0 +1 @@ +{"mean_stat": [533749178.75492024, 537379151.9412827, 553560684.251823, 587164297.7995199, 631868827.5506272, 662598279.7375823, 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a/modules/audio/asr/u2_conformer_aishell/module.py b/modules/audio/asr/u2_conformer_aishell/module.py new file mode 100644 index 00000000..8ce72804 --- /dev/null +++ b/modules/audio/asr/u2_conformer_aishell/module.py @@ -0,0 +1,73 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from pathlib import Path +import sys + +import numpy as np +from paddlehub.env import MODULE_HOME +from paddlehub.module.module import moduleinfo, serving +from paddlehub.utils.log import logger + +import paddle +import soundfile as sf + +# TODO: Remove system path when deepspeech can be installed via pip. +sys.path.append(os.path.join(MODULE_HOME, 'u2_conformer_aishell')) +from deepspeech.exps.u2.config import get_cfg_defaults +from deepspeech.utils.utility import UpdateConfig +from .u2_conformer_tester import U2ConformerTester + + +@moduleinfo(name="u2_conformer_aishell", version="1.0.0", summary="", author="Baidu", author_email="", type="audio/asr") +class U2Conformer(paddle.nn.Layer): + def __init__(self): + super(U2Conformer, self).__init__() + + # resource + res_dir = os.path.join(MODULE_HOME, 'u2_conformer_aishell', 'assets') + conf_file = os.path.join(res_dir, 'conf/conformer.yaml') + checkpoint = os.path.join(res_dir, 'checkpoints/avg_20.pdparams') + + # config + self.config = get_cfg_defaults() + self.config.merge_from_file(conf_file) + + # TODO: Remove path updating snippet. + with UpdateConfig(self.config): + self.config.collator.vocab_filepath = os.path.join(res_dir, self.config.collator.vocab_filepath) + # self.config.collator.spm_model_prefix = os.path.join(res_dir, self.config.collator.spm_model_prefix) + self.config.collator.augmentation_config = os.path.join(res_dir, self.config.collator.augmentation_config) + self.config.model.cmvn_file = os.path.join(res_dir, self.config.model.cmvn_file) + self.config.decoding.decoding_method = 'attention_rescoring' + self.config.decoding.batch_size = 1 + + # model + self.tester = U2ConformerTester(self.config) + self.tester.setup_model() + self.tester.resume(checkpoint) + + @staticmethod + def check_audio(audio_file): + sig, sample_rate = sf.read(audio_file) + assert sample_rate == 16000, 'Excepting sample rate of input audio is 16000, but got {}'.format(sample_rate) + + @serving + def speech_recognize(self, audio_file, device='cpu'): + assert os.path.isfile(audio_file), 'File not exists: {}'.format(audio_file) + self.check_audio(audio_file) + + paddle.set_device(device) + return self.tester.test(audio_file)[0][0] diff --git a/modules/audio/asr/u2_conformer_aishell/requirements.txt b/modules/audio/asr/u2_conformer_aishell/requirements.txt new file mode 100644 index 00000000..49fb307f --- /dev/null +++ b/modules/audio/asr/u2_conformer_aishell/requirements.txt @@ -0,0 +1,12 @@ +loguru +yacs +jsonlines +scipy==1.2.1 +sentencepiece +resampy==0.2.2 +SoundFile==0.9.0.post1 +soxbindings +kaldiio +typeguard +editdistance +textgrid diff --git a/modules/audio/asr/u2_conformer_aishell/u2_conformer_tester.py b/modules/audio/asr/u2_conformer_aishell/u2_conformer_tester.py new file mode 100644 index 00000000..c4f8d470 --- /dev/null +++ b/modules/audio/asr/u2_conformer_aishell/u2_conformer_tester.py @@ -0,0 +1,80 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Evaluation for U2 model.""" +import os +import sys + +import paddle + +from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer +from deepspeech.io.collator import SpeechCollator +from deepspeech.models.u2 import U2Model +from deepspeech.utils import mp_tools +from deepspeech.utils.utility import UpdateConfig + + +class U2ConformerTester: + def __init__(self, config): + self.config = config + self.collate_fn_test = SpeechCollator.from_config(config) + self._text_featurizer = TextFeaturizer( + unit_type=config.collator.unit_type, vocab_filepath=None, spm_model_prefix=config.collator.spm_model_prefix) + + @mp_tools.rank_zero_only + @paddle.no_grad() + def test(self, audio_file): + self.model.eval() + cfg = self.config.decoding + collate_fn_test = self.collate_fn_test + audio, _ = collate_fn_test.process_utterance(audio_file=audio_file, transcript="Hello") + audio_len = audio.shape[0] + audio = paddle.to_tensor(audio, dtype='float32') + audio_len = paddle.to_tensor(audio_len) + audio = paddle.unsqueeze(audio, axis=0) + vocab_list = collate_fn_test.vocab_list + + text_feature = self.collate_fn_test.text_feature + result_transcripts = self.model.decode( + audio, + audio_len, + text_feature=text_feature, + decoding_method=cfg.decoding_method, + lang_model_path=cfg.lang_model_path, + beam_alpha=cfg.alpha, + beam_beta=cfg.beta, + beam_size=cfg.beam_size, + cutoff_prob=cfg.cutoff_prob, + cutoff_top_n=cfg.cutoff_top_n, + num_processes=cfg.num_proc_bsearch, + ctc_weight=cfg.ctc_weight, + decoding_chunk_size=cfg.decoding_chunk_size, + num_decoding_left_chunks=cfg.num_decoding_left_chunks, + simulate_streaming=cfg.simulate_streaming) + + return result_transcripts + + def setup_model(self): + config = self.config.clone() + with UpdateConfig(config): + config.model.input_dim = self.collate_fn_test.feature_size + config.model.output_dim = self.collate_fn_test.vocab_size + + self.model = U2Model.from_config(config.model) + + def resume(self, checkpoint): + """Resume from the checkpoint at checkpoints in the output + directory or load a specified checkpoint. + """ + model_dict = paddle.load(checkpoint) + self.model.set_state_dict(model_dict) diff --git a/modules/audio/asr/u2_conformer_librispeech/README.md b/modules/audio/asr/u2_conformer_librispeech/README.md new file mode 100644 index 00000000..f16da3f5 --- /dev/null +++ b/modules/audio/asr/u2_conformer_librispeech/README.md @@ -0,0 +1,156 @@ +# u2_conformer_librispeech + +|模型名称|u2_conformer_librispeech| +| :--- | :---: | +|类别|语音-语音识别| +|网络|DeepSpeech2| +|数据集|LibriSpeech| +|是否支持Fine-tuning|否| +|模型大小|191MB| +|最新更新日期|2021-11-01| +|数据指标|英文WER 0.034| + +## 一、模型基本信息 + +### 模型介绍 + +U2 Conformer模型是一种适用于英文和中文的end-to-end语音识别模型。u2_conformer_libirspeech采用了conformer的encoder和transformer的decoder的模型结构，并且使用了ctc-prefix beam search的方式进行一遍打分，再利用attention decoder进行二次打分的方式进行解码来得到最终结果。 + +u2_conformer_libirspeech在英文开源语音数据集[LibriSpeech ASR corpus](http://www.openslr.org/12/)进行了预训练，该模型在其测试集上的WER指标是0.034655。 + +

+
+

+ +

+
+

+ +更多详情请参考: +- [Unified Streaming and Non-streaming Two-pass End-to-end Model for Speech Recognition](https://arxiv.org/abs/2012.05481) +- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100) + +## 二、安装 + +- ### 1、系统依赖 + + - libsndfile + - Linux + ```shell + $ sudo apt-get install libsndfile + or + $ sudo yum install libsndfile + ``` + - MacOs + ``` + $ brew install libsndfile + ``` + +- ### 2、环境依赖 + + - paddlepaddle >= 2.1.0 + + - paddlehub >= 2.1.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst) + +- ### 3、安装 + + - ```shell + $ hub install u2_conformer_librispeech + ``` + - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md) + | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md) + + +## 三、模型API预测 + +- ### 1、预测代码示例 + + - ```python + import paddlehub as hub + + # 采样率为16k，格式为wav的英文语音音频 + wav_file = '/PATH/TO/AUDIO' + + model = hub.Module( + name='u2_conformer_librispeech', + version='1.0.0') + text = model.speech_recognize(wav_file) + + print(text) + ``` + +- ### 2、API + - ```python + def check_audio(audio_file) + ``` + - 检查输入音频格式和采样率是否满足为16000 + + - **参数** + + - `audio_file`：本地音频文件(*.wav)的路径，如`/path/to/input.wav` + + - ```python + def speech_recognize( + audio_file, + device='cpu', + ) + ``` + - 将输入的音频识别成文字 + + - **参数** + + - `audio_file`：本地音频文件(*.wav)的路径，如`/path/to/input.wav` + - `device`：预测时使用的设备，默认为`cpu`，如需使用gpu预测，请设置为`gpu`。 + + - **返回** + + - `text`：str类型，返回输入音频的识别文字结果。 + + +## 四、服务部署 + +- PaddleHub Serving可以部署一个在线的语音识别服务。 + +- ### 第一步：启动PaddleHub Serving + + - ```shell + $ hub serving start -m u2_conformer_librispeech + ``` + + - 这样就完成了一个语音识别服务化API的部署，默认端口号为8866。 + + - **NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA_VISIBLE_DEVICES环境变量，否则不用设置。 + +- ### 第二步：发送预测请求 + + - 配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果 + + - ```python + import requests + import json + + # 需要识别的音频的存放路径，确保部署服务的机器可访问 + file = '/path/to/input.wav' + + # 以key的方式指定text传入预测方法的时的参数，此例中为"audio_file" + data = {"audio_file": file} + + # 发送post请求，content-type类型应指定json方式，url中的ip地址需改为对应机器的ip + url = "http://127.0.0.1:8866/predict/u2_conformer_librispeech" + + # 指定post请求的headers为application/json方式 + headers = {"Content-Type": "application/json"} + + r = requests.post(url=url, headers=headers, data=json.dumps(data)) + print(r.json()) + ``` + +## 五、更新历史 + +* 1.0.0 + + 初始发布 + + ```shell + $ hub install u2_conformer_librispeech + ``` diff --git a/modules/audio/asr/u2_conformer_librispeech/__init__.py b/modules/audio/asr/u2_conformer_librispeech/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/modules/audio/asr/u2_conformer_librispeech/assets/conf/augmentation.json b/modules/audio/asr/u2_conformer_librispeech/assets/conf/augmentation.json new file mode 100644 index 00000000..0967ef42 --- /dev/null +++ b/modules/audio/asr/u2_conformer_librispeech/assets/conf/augmentation.json @@ -0,0 +1 @@ +{} diff --git a/modules/audio/asr/u2_conformer_librispeech/assets/conf/conformer.yaml b/modules/audio/asr/u2_conformer_librispeech/assets/conf/conformer.yaml new file mode 100644 index 00000000..72342e44 --- /dev/null +++ b/modules/audio/asr/u2_conformer_librispeech/assets/conf/conformer.yaml @@ -0,0 +1,116 @@ +# https://yaml.org/type/float.html +data: + train_manifest: data/manifest.test-clean + dev_manifest: data/manifest.test-clean + test_manifest: data/manifest.test-clean + min_input_len: 0.5 # seconds + max_input_len: 30.0 # seconds + min_output_len: 0.0 # tokens + max_output_len: 400.0 # tokens + min_output_input_ratio: 0.05 + max_output_input_ratio: 100.0 + +collator: + vocab_filepath: data/vocab.txt + unit_type: 'spm' + spm_model_prefix: 'data/bpe_unigram_5000' + mean_std_filepath: "" + augmentation_config: conf/augmentation.json + batch_size: 16 + raw_wav: True # use raw_wav or kaldi feature + spectrum_type: fbank #linear, mfcc, fbank + feat_dim: 80 + delta_delta: False + dither: 1.0 + target_sample_rate: 16000 + max_freq: None + n_fft: None + stride_ms: 10.0 + window_ms: 25.0 + use_dB_normalization: True + target_dB: -20 + random_seed: 0 + keep_transcription_text: False + sortagrad: True + shuffle_method: batch_shuffle + num_workers: 2 + + +# network architecture +model: + cmvn_file: "data/mean_std.json" + cmvn_file_type: "json" + # encoder related + encoder: conformer + encoder_conf: + output_size: 256 # dimension of attention + attention_heads: 4 + linear_units: 2048 # the number of units of position-wise feed forward + num_blocks: 12 # the number of encoder blocks + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.0 + input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8 + normalize_before: True + use_cnn_module: True + cnn_module_kernel: 15 + activation_type: 'swish' + pos_enc_layer_type: 'rel_pos' + selfattention_layer_type: 'rel_selfattn' + + # decoder related + decoder: transformer + decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 6 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.0 + src_attention_dropout_rate: 0.0 + + # hybrid CTC/attention + model_conf: + ctc_weight: 0.3 + ctc_dropoutrate: 0.0 + ctc_grad_norm_type: instance + lsm_weight: 0.1 # label smoothing option + length_normalized_loss: false + + +training: + n_epoch: 120 + accum_grad: 8 + global_grad_clip: 3.0 + optim: adam + optim_conf: + lr: 0.004 + weight_decay: 1e-06 + scheduler: warmuplr # pytorch v1.1.0+ required + scheduler_conf: + warmup_steps: 25000 + lr_decay: 1.0 + log_interval: 100 + checkpoint: + kbest_n: 50 + latest_n: 5 + + +decoding: + batch_size: 64 + error_rate_type: wer + decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring' + lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm + alpha: 2.5 + beta: 0.3 + beam_size: 10 + cutoff_prob: 1.0 + cutoff_top_n: 0 + num_proc_bsearch: 8 + ctc_weight: 0.5 # ctc weight for attention rescoring decode mode. + decoding_chunk_size: -1 # decoding chunk size. 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+▁view +▁vigorous +▁village +▁villain +▁villefort +▁vine +▁violence +▁violent +▁violet +▁virgin +▁virginia +▁virtue +▁virtuous +▁visible +▁vision +▁visit +▁visitor +▁vital +▁vivid +▁vo +▁voice +▁vol +▁volume +▁volunteer +▁vote +▁vow +▁voyage +▁vulgar +▁w +▁wa +▁wag +▁wagon +▁waist +▁waistcoat +▁wait +▁waited +▁waiting +▁wake +▁wal +▁walk +▁walked +▁walking +▁wall +▁walls +▁walter +▁wander +▁wandering +▁want +▁wanted +▁war +▁warm +▁warn +▁warning +▁warrant +▁warrior +▁was +▁wash +▁washington +▁watch +▁watched +▁watching +▁water +▁wave +▁waves +▁waving +▁wax +▁way +▁ways +▁we +▁weak +▁weakness +▁wealth +▁weapon +▁wear +▁weary +▁weather +▁wedding +▁week +▁weeks +▁weep +▁weigh +▁weight +▁welcome +▁welfare +▁well +▁went +▁wept +▁were +▁west +▁western +▁wh +▁whale +▁what +▁whatever +▁wheat +▁wheel +▁when +▁whence +▁where +▁wherefore +▁whereupon +▁whether +▁whi +▁which +▁while +▁whilst +▁whip +▁whirl +▁whisk +▁whisper +▁whispered +▁whistle +▁white +▁whither +▁who +▁whole +▁wholly +▁whom +▁whose +▁why +▁wi +▁wicked +▁wide +▁widow +▁wife +▁wild +▁wilderness +▁will +▁william +▁willing +▁wilson +▁wilt +▁win +▁wind +▁window +▁windows +▁wine +▁wings +▁winter +▁wip +▁wire +▁wisdom +▁wise +▁wish +▁wished +▁wishes +▁wit +▁witch +▁with +▁withdraw +▁withdrew +▁within +▁without +▁witness +▁wives +▁woe +▁woke +▁wolf +▁wolves +▁woman +▁women +▁won +▁wonder +▁wondered +▁wonderful +▁wondering +▁wood +▁wooden +▁woods +▁word +▁words +▁wore +▁work +▁worked +▁working +▁world +▁worm +▁worn +▁worried +▁worry +▁worse +▁worship +▁worst +▁worth +▁worthy +▁would +▁wouldn +▁wound +▁wounded +▁wrap +▁wrapped +▁wrath +▁wreck +▁wren +▁wretch +▁wretched +▁wrinkl +▁wrist +▁write +▁writer +▁writing +▁written +▁wrong +▁wrote +▁wrought +▁ya +▁yard +▁ye +▁year +▁years +▁yellow +▁yes +▁yesterday +▁yet +▁yield +▁yo +▁yonder +▁york +▁you +▁young +▁your +▁yourself +▁yourselves +▁youth + diff --git a/modules/audio/asr/u2_conformer_librispeech/module.py b/modules/audio/asr/u2_conformer_librispeech/module.py new file mode 100644 index 00000000..b98277f5 --- /dev/null +++ b/modules/audio/asr/u2_conformer_librispeech/module.py @@ -0,0 +1,74 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from pathlib import Path +import sys + +import numpy as np +from paddlehub.env import MODULE_HOME +from paddlehub.module.module import moduleinfo, serving +from paddlehub.utils.log import logger + +import paddle +import soundfile as sf + +# TODO: Remove system path when deepspeech can be installed via pip. +sys.path.append(os.path.join(MODULE_HOME, 'u2_conformer_librispeech')) +from deepspeech.exps.u2.config import get_cfg_defaults +from deepspeech.utils.utility import UpdateConfig +from .u2_conformer_tester import U2ConformerTester + + +@moduleinfo( + name="u2_conformer_librispeech", version="1.0.0", summary="", author="Baidu", author_email="", type="audio/asr") +class U2Conformer(paddle.nn.Layer): + def __init__(self): + super(U2Conformer, self).__init__() + + # resource + res_dir = os.path.join(MODULE_HOME, 'u2_conformer_librispeech', 'assets') + conf_file = os.path.join(res_dir, 'conf/conformer.yaml') + checkpoint = os.path.join(res_dir, 'checkpoints/avg_30.pdparams') + + # config + self.config = get_cfg_defaults() + self.config.merge_from_file(conf_file) + + # TODO: Remove path updating snippet. + with UpdateConfig(self.config): + self.config.collator.vocab_filepath = os.path.join(res_dir, self.config.collator.vocab_filepath) + self.config.collator.spm_model_prefix = os.path.join(res_dir, self.config.collator.spm_model_prefix) + self.config.collator.augmentation_config = os.path.join(res_dir, self.config.collator.augmentation_config) + self.config.model.cmvn_file = os.path.join(res_dir, self.config.model.cmvn_file) + self.config.decoding.decoding_method = 'attention_rescoring' + self.config.decoding.batch_size = 1 + + # model + self.tester = U2ConformerTester(self.config) + self.tester.setup_model() + self.tester.resume(checkpoint) + + @staticmethod + def check_audio(audio_file): + sig, sample_rate = sf.read(audio_file) + assert sample_rate == 16000, 'Excepting sample rate of input audio is 16000, but got {}'.format(sample_rate) + + @serving + def speech_recognize(self, audio_file, device='cpu'): + assert os.path.isfile(audio_file), 'File not exists: {}'.format(audio_file) + self.check_audio(audio_file) + + paddle.set_device(device) + return self.tester.test(audio_file)[0][0] diff --git a/modules/audio/asr/u2_conformer_librispeech/requirements.txt b/modules/audio/asr/u2_conformer_librispeech/requirements.txt new file mode 100644 index 00000000..49fb307f --- /dev/null +++ b/modules/audio/asr/u2_conformer_librispeech/requirements.txt @@ -0,0 +1,12 @@ +loguru +yacs +jsonlines +scipy==1.2.1 +sentencepiece +resampy==0.2.2 +SoundFile==0.9.0.post1 +soxbindings +kaldiio +typeguard +editdistance +textgrid diff --git a/modules/audio/asr/u2_conformer_librispeech/u2_conformer_tester.py b/modules/audio/asr/u2_conformer_librispeech/u2_conformer_tester.py new file mode 100644 index 00000000..c4f8d470 --- /dev/null +++ b/modules/audio/asr/u2_conformer_librispeech/u2_conformer_tester.py @@ -0,0 +1,80 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Evaluation for U2 model.""" +import os +import sys + +import paddle + +from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer +from deepspeech.io.collator import SpeechCollator +from deepspeech.models.u2 import U2Model +from deepspeech.utils import mp_tools +from deepspeech.utils.utility import UpdateConfig + + +class U2ConformerTester: + def __init__(self, config): + self.config = config + self.collate_fn_test = SpeechCollator.from_config(config) + self._text_featurizer = TextFeaturizer( + unit_type=config.collator.unit_type, vocab_filepath=None, spm_model_prefix=config.collator.spm_model_prefix) + + @mp_tools.rank_zero_only + @paddle.no_grad() + def test(self, audio_file): + self.model.eval() + cfg = self.config.decoding + collate_fn_test = self.collate_fn_test + audio, _ = collate_fn_test.process_utterance(audio_file=audio_file, transcript="Hello") + audio_len = audio.shape[0] + audio = paddle.to_tensor(audio, dtype='float32') + audio_len = paddle.to_tensor(audio_len) + audio = paddle.unsqueeze(audio, axis=0) + vocab_list = collate_fn_test.vocab_list + + text_feature = self.collate_fn_test.text_feature + result_transcripts = self.model.decode( + audio, + audio_len, + text_feature=text_feature, + decoding_method=cfg.decoding_method, + lang_model_path=cfg.lang_model_path, + beam_alpha=cfg.alpha, + beam_beta=cfg.beta, + beam_size=cfg.beam_size, + cutoff_prob=cfg.cutoff_prob, + cutoff_top_n=cfg.cutoff_top_n, + num_processes=cfg.num_proc_bsearch, + ctc_weight=cfg.ctc_weight, + decoding_chunk_size=cfg.decoding_chunk_size, + num_decoding_left_chunks=cfg.num_decoding_left_chunks, + simulate_streaming=cfg.simulate_streaming) + + return result_transcripts + + def setup_model(self): + config = self.config.clone() + with UpdateConfig(config): + config.model.input_dim = self.collate_fn_test.feature_size + config.model.output_dim = self.collate_fn_test.vocab_size + + self.model = U2Model.from_config(config.model) + + def resume(self, checkpoint): + """Resume from the checkpoint at checkpoints in the output + directory or load a specified checkpoint. + """ + model_dict = paddle.load(checkpoint) + self.model.set_state_dict(model_dict) diff --git a/modules/audio/audio_classification/PANNs/cnn10/README.md b/modules/audio/audio_classification/PANNs/cnn10/README.md index 9dd7c78f..c6ce4c55 100644 --- a/modules/audio/audio_classification/PANNs/cnn10/README.md +++ b/modules/audio/audio_classification/PANNs/cnn10/README.md @@ -1,68 +1,52 @@ -```shell -$ hub install panns_cnn10==1.0.0 -``` +# panns_cnn10 +|模型名称|panns_cnn10| +| :--- | :---: | +|类别|语音-声音分类| +|网络|PANNs| +|数据集|Google Audioset| +|是否支持Fine-tuning|是| +|模型大小|31MB| +|最新更新日期|2021-06-15| +|数据指标|mAP 0.380| + +## 一、模型基本信息 + +### 模型介绍 `panns_cnn10`是一个基于[Google Audioset](https://research.google.com/audioset/)数据集训练的声音分类/识别的模型。该模型主要包含8个卷积层和2个全连接层，模型参数为4.9M。经过预训练后，可以用于提取音频的embbedding，维度是512。 更多详情请参考论文：[PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://arxiv.org/pdf/1912.10211.pdf) -## API -```python -def __init__( - task, - num_class=None, - label_map=None, - load_checkpoint=None, - **kwargs, -) -``` - -创建Module对象。 - -**参数** - -* `task`： 任务名称，可为`sound-cls`或者`None`。`sound-cls`代表声音分类任务，可以对声音分类的数据集进行finetune；为`None`时可以获取预训练模型对音频进行分类/Tagging。 -* `num_classes`：声音分类任务的类别数，finetune时需要指定，数值与具体使用的数据集类别数一致。 -* `label_map`：预测时的类别映射表。 -* `load_checkpoint`：使用PaddleHub Fine-tune api训练保存的模型参数文件路径。 -* `**kwargs`：用户额外指定的关键字字典类型的参数。 - -```python -def predict( - data, - sample_rate, - batch_size=1, - feat_type='mel', - use_gpu=False -) -``` +## 二、安装 + +- ### 1、环境依赖 + + - paddlepaddle >= 2.0.0 -**参数** + - paddlehub >= 2.0.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst) -* `data`： 待预测数据，格式为\[waveform1, wavwform2…,\]，其中每个元素都是一个一维numpy列表，是音频的波形采样数值列表。 -* `sample_rate`：音频文件的采样率。 -* `feat_type`：音频特征的种类选取，当前支持`'mel'`(详情可查看[Mel-frequency cepstrum](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum))和原始波形特征`'raw'`。 -* `batch_size`：模型批处理大小。 -* `use_gpu`：是否使用gpu，默认为False。对于GPU用户，建议开启use_gpu。 +- ### 2、安装 -**返回** + - ```shell + $ hub install panns_cnn10 + ``` + - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md) + | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md) -* `results`：list类型，不同任务类型的返回结果如下 - * 声音分类(task参数为`sound-cls`)：列表里包含每个音频文件的分类标签。 - * Tagging(task参数为`None`)：列表里包含每个音频文件527个类别([Audioset标签](https://research.google.com/audioset/))的得分。 +## 三、模型API预测 -**代码示例** +- ### 1、预测代码示例 -- [ESC50](https://github.com/karolpiczak/ESC-50)声音分类预测 - ```python + - ```python + # ESC50声音分类预测 import librosa import paddlehub as hub from paddlehub.datasets import ESC50 sr = 44100 # 音频文件的采样率 - wav_file = '/data/cat.wav' # 用于预测的音频文件路径 + wav_file = '/PATH/TO/AUDIO' # 用于预测的音频文件路径 checkpoint = 'model.pdparams' # 用于预测的模型参数 label_map = {idx: label for idx, label in enumerate(ESC50.label_list)} @@ -86,8 +70,8 @@ def predict( print('File: {}\tLable: {}'.format(wav_file, result[0])) ``` -- Audioset Tagging - ```python + - ```python + # Audioset Tagging import librosa import numpy as np import paddlehub as hub @@ -105,7 +89,7 @@ def predict( print(msg) sr = 44100 # 音频文件的采样率 - wav_file = '/data/cat.wav' # 用于预测的音频文件路径 + wav_file = '/PATH/TO/AUDIO' # 用于预测的音频文件路径 label_file = './audioset_labels.txt' # audioset标签文本文件 topk = 10 # 展示的topk数 @@ -130,23 +114,58 @@ def predict( show_topk(topk, label_map, wav_file, result[0]) ``` -详情可参考PaddleHub示例： -- [AudioClassification](https://github.com/PaddlePaddle/PaddleHub/tree/release/v2.0/demo/audio_classification) - - -## 查看代码 - -https://github.com/qiuqiangkong/audioset_tagging_cnn +- ### 2、API + - ```python + def __init__( + task, + num_class=None, + label_map=None, + load_checkpoint=None, + **kwargs, + ) + ``` + - 创建Module对象。 + + - **参数** + - `task`： 任务名称，可为`sound-cls`或者`None`。`sound-cls`代表声音分类任务，可以对声音分类的数据集进行finetune；为`None`时可以获取预训练模型对音频进行分类/Tagging。 + - `num_classes`：声音分类任务的类别数，finetune时需要指定，数值与具体使用的数据集类别数一致。 + - `label_map`：预测时的类别映射表。 + - `load_checkpoint`：使用PaddleHub Fine-tune api训练保存的模型参数文件路径。 + - `**kwargs`：用户额外指定的关键字字典类型的参数。 + + - ```python + def predict( + data, + sample_rate, + batch_size=1, + feat_type='mel', + use_gpu=False + ) + ``` + - 模型预测，输入为音频波形数据，输出为分类标签。 -## 依赖 + - **参数** + - `data`： 待预测数据，格式为\[waveform1, wavwform2…,\]，其中每个元素都是一个一维numpy列表，是音频的波形采样数值列表。 + - `sample_rate`：音频文件的采样率。 + - `feat_type`：音频特征的种类选取，当前支持`'mel'`(详情可查看[Mel-frequency cepstrum](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum))和原始波形特征`'raw'`。 + - `batch_size`：模型批处理大小。 + - `use_gpu`：是否使用gpu，默认为False。对于GPU用户，建议开启use_gpu。 -paddlepaddle >= 2.0.0 + - **返回** + - `results`：list类型，不同任务类型的返回结果如下 + - 声音分类(task参数为`sound-cls`)：列表里包含每个音频文件的分类标签。 + - Tagging(task参数为`None`)：列表里包含每个音频文件527个类别([Audioset标签](https://research.google.com/audioset/))的得分。 -paddlehub >= 2.0.0 + 详情可参考PaddleHub示例： + - [AudioClassification](https://github.com/PaddlePaddle/PaddleHub/tree/release/v2.0/demo/audio_classification) -## 更新历史 +## 四、更新历史 * 1.0.0 初始发布，动态图版本模型，支持声音分类`sound-cls`任务的fine-tune和基于Audioset Tagging预测。 + + ```shell + $ hub install panns_cnn10 + ``` diff --git a/modules/audio/audio_classification/PANNs/cnn14/README.md b/modules/audio/audio_classification/PANNs/cnn14/README.md index adb66f9c..c65e7bea 100644 --- a/modules/audio/audio_classification/PANNs/cnn14/README.md +++ b/modules/audio/audio_classification/PANNs/cnn14/README.md @@ -1,68 +1,52 @@ -```shell -$ hub install panns_cnn14==1.0.0 -``` +# panns_cnn14 +|模型名称|panns_cnn14| +| :--- | :---: | +|类别|语音-声音分类| +|网络|PANNs| +|数据集|Google Audioset| +|是否支持Fine-tuning|是| +|模型大小|469MB| +|最新更新日期|2021-06-15| +|数据指标|mAP 0.431| + +## 一、模型基本信息 + +### 模型介绍 `panns_cnn14`是一个基于[Google Audioset](https://research.google.com/audioset/)数据集训练的声音分类/识别的模型。该模型主要包含12个卷积层和2个全连接层，模型参数为79.6M。经过预训练后，可以用于提取音频的embbedding，维度是2048。 更多详情请参考论文：[PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://arxiv.org/pdf/1912.10211.pdf) -## API -```python -def __init__( - task, - num_class=None, - label_map=None, - load_checkpoint=None, - **kwargs, -) -``` - -创建Module对象。 - -**参数** - -* `task`： 任务名称，可为`sound-cls`或者`None`。`sound-cls`代表声音分类任务，可以对声音分类的数据集进行finetune；为`None`时可以获取预训练模型对音频进行分类/Tagging。 -* `num_classes`：声音分类任务的类别数，finetune时需要指定，数值与具体使用的数据集类别数一致。 -* `label_map`：预测时的类别映射表。 -* `load_checkpoint`：使用PaddleHub Fine-tune api训练保存的模型参数文件路径。 -* `**kwargs`：用户额外指定的关键字字典类型的参数。 - -```python -def predict( - data, - sample_rate, - batch_size=1, - feat_type='mel', - use_gpu=False -) -``` +## 二、安装 + +- ### 1、环境依赖 + + - paddlepaddle >= 2.0.0 -**参数** + - paddlehub >= 2.0.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst) -* `data`： 待预测数据，格式为\[waveform1, wavwform2…,\]，其中每个元素都是一个一维numpy列表，是音频的波形采样数值列表。 -* `sample_rate`：音频文件的采样率。 -* `feat_type`：音频特征的种类选取，当前支持`'mel'`(详情可查看[Mel-frequency cepstrum](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum))和原始波形特征`'raw'`。 -* `batch_size`：模型批处理大小。 -* `use_gpu`：是否使用gpu，默认为False。对于GPU用户，建议开启use_gpu。 +- ### 2、安装 -**返回** + - ```shell + $ hub install panns_cnn14 + ``` + - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md) + | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md) -* `results`：list类型，不同任务类型的返回结果如下 - * 声音分类(task参数为`sound-cls`)：列表里包含每个音频文件的分类标签。 - * Tagging(task参数为`None`)：列表里包含每个音频文件527个类别([Audioset标签](https://research.google.com/audioset/))的得分。 +## 三、模型API预测 -**代码示例** +- ### 1、预测代码示例 -- [ESC50](https://github.com/karolpiczak/ESC-50)声音分类预测 - ```python + - ```python + # ESC50声音分类预测 import librosa import paddlehub as hub from paddlehub.datasets import ESC50 sr = 44100 # 音频文件的采样率 - wav_file = '/data/cat.wav' # 用于预测的音频文件路径 + wav_file = '/PATH/TO/AUDIO' # 用于预测的音频文件路径 checkpoint = 'model.pdparams' # 用于预测的模型参数 label_map = {idx: label for idx, label in enumerate(ESC50.label_list)} @@ -86,8 +70,8 @@ def predict( print('File: {}\tLable: {}'.format(wav_file, result[0])) ``` -- Audioset Tagging - ```python + - ```python + # Audioset Tagging import librosa import numpy as np import paddlehub as hub @@ -105,7 +89,7 @@ def predict( print(msg) sr = 44100 # 音频文件的采样率 - wav_file = '/data/cat.wav' # 用于预测的音频文件路径 + wav_file = '/PATH/TO/AUDIO' # 用于预测的音频文件路径 label_file = './audioset_labels.txt' # audioset标签文本文件 topk = 10 # 展示的topk数 @@ -130,23 +114,58 @@ def predict( show_topk(topk, label_map, wav_file, result[0]) ``` -详情可参考PaddleHub示例： -- [AudioClassification](https://github.com/PaddlePaddle/PaddleHub/tree/release/v2.0/demo/audio_classification) - - -## 查看代码 - -https://github.com/qiuqiangkong/audioset_tagging_cnn +- ### 2、API + - ```python + def __init__( + task, + num_class=None, + label_map=None, + load_checkpoint=None, + **kwargs, + ) + ``` + - 创建Module对象。 + + - **参数** + - `task`： 任务名称，可为`sound-cls`或者`None`。`sound-cls`代表声音分类任务，可以对声音分类的数据集进行finetune；为`None`时可以获取预训练模型对音频进行分类/Tagging。 + - `num_classes`：声音分类任务的类别数，finetune时需要指定，数值与具体使用的数据集类别数一致。 + - `label_map`：预测时的类别映射表。 + - `load_checkpoint`：使用PaddleHub Fine-tune api训练保存的模型参数文件路径。 + - `**kwargs`：用户额外指定的关键字字典类型的参数。 + + - ```python + def predict( + data, + sample_rate, + batch_size=1, + feat_type='mel', + use_gpu=False + ) + ``` + - 模型预测，输入为音频波形数据，输出为分类标签。 -## 依赖 + - **参数** + - `data`： 待预测数据，格式为\[waveform1, wavwform2…,\]，其中每个元素都是一个一维numpy列表，是音频的波形采样数值列表。 + - `sample_rate`：音频文件的采样率。 + - `feat_type`：音频特征的种类选取，当前支持`'mel'`(详情可查看[Mel-frequency cepstrum](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum))和原始波形特征`'raw'`。 + - `batch_size`：模型批处理大小。 + - `use_gpu`：是否使用gpu，默认为False。对于GPU用户，建议开启use_gpu。 -paddlepaddle >= 2.0.0 + - **返回** + - `results`：list类型，不同任务类型的返回结果如下 + - 声音分类(task参数为`sound-cls`)：列表里包含每个音频文件的分类标签。 + - Tagging(task参数为`None`)：列表里包含每个音频文件527个类别([Audioset标签](https://research.google.com/audioset/))的得分。 -paddlehub >= 2.0.0 + 详情可参考PaddleHub示例： + - [AudioClassification](https://github.com/PaddlePaddle/PaddleHub/tree/release/v2.0/demo/audio_classification) -## 更新历史 +## 四、更新历史 * 1.0.0 初始发布，动态图版本模型，支持声音分类`sound-cls`任务的fine-tune和基于Audioset Tagging预测。 + + ```shell + $ hub install panns_cnn14 + ``` diff --git a/modules/audio/audio_classification/PANNs/cnn6/README.md b/modules/audio/audio_classification/PANNs/cnn6/README.md index dd10c0b2..0e8b9442 100644 --- a/modules/audio/audio_classification/PANNs/cnn6/README.md +++ b/modules/audio/audio_classification/PANNs/cnn6/README.md @@ -1,68 +1,52 @@ -```shell -$ hub install panns_cnn6==1.0.0 -``` +# panns_cnn6 +|模型名称|panns_cnn6| +| :--- | :---: | +|类别|语音-声音分类| +|网络|PANNs| +|数据集|Google Audioset| +|是否支持Fine-tuning|是| +|模型大小|29MB| +|最新更新日期|2021-06-15| +|数据指标|mAP 0.343| -`panns_cnn6`是一个基于[Google Audioset](https://research.google.com/audioset/)数据集训练的声音分类/识别的模型。该模型主要包含4个卷积层和2个全连接层，模型参数为4.5M。经过预训练后，可以用于提取音频的embbedding，维度是512。 +## 一、模型基本信息 -更多详情请参考论文：[PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://arxiv.org/pdf/1912.10211.pdf) +### 模型介绍 -## API -```python -def __init__( - task, - num_class=None, - label_map=None, - load_checkpoint=None, - **kwargs, -) -``` +`panns_cnn6`是一个基于[Google Audioset](https://research.google.com/audioset/)数据集训练的声音分类/识别的模型。该模型主要包含4个卷积层和2个全连接层，模型参数为4.5M。经过预训练后，可以用于提取音频的embbedding，维度是512。 -创建Module对象。 +更多详情请参考：[PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://arxiv.org/pdf/1912.10211.pdf) -**参数** +## 二、安装 -* `task`： 任务名称，可为`sound-cls`或者`None`。`sound-cls`代表声音分类任务，可以对声音分类的数据集进行finetune；为`None`时可以获取预训练模型对音频进行分类/Tagging。 -* `num_classes`：声音分类任务的类别数，finetune时需要指定，数值与具体使用的数据集类别数一致。 -* `label_map`：预测时的类别映射表。 -* `load_checkpoint`：使用PaddleHub Fine-tune api训练保存的模型参数文件路径。 -* `**kwargs`：用户额外指定的关键字字典类型的参数。 +- ### 1、环境依赖 -```python -def predict( - data, - sample_rate, - batch_size=1, - feat_type='mel', - use_gpu=False -) -``` + - paddlepaddle >= 2.0.0 -**参数** + - paddlehub >= 2.0.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst) -* `data`： 待预测数据，格式为\[waveform1, wavwform2…,\]，其中每个元素都是一个一维numpy列表，是音频的波形采样数值列表。 -* `sample_rate`：音频文件的采样率。 -* `feat_type`：音频特征的种类选取，当前支持`'mel'`(详情可查看[Mel-frequency cepstrum](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum))和原始波形特征`'raw'`。 -* `batch_size`：模型批处理大小。 -* `use_gpu`：是否使用gpu，默认为False。对于GPU用户，建议开启use_gpu。 +- ### 2、安装 -**返回** + - ```shell + $ hub install panns_cnn6 + ``` + - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md) + | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md) -* `results`：list类型，不同任务类型的返回结果如下 - * 声音分类(task参数为`sound-cls`)：列表里包含每个音频文件的分类标签。 - * Tagging(task参数为`None`)：列表里包含每个音频文件527个类别([Audioset标签](https://research.google.com/audioset/))的得分。 +## 三、模型API预测 -**代码示例** +- ### 1、预测代码示例 -- [ESC50](https://github.com/karolpiczak/ESC-50)声音分类预测 - ```python + - ```python + # ESC50声音分类预测 import librosa import paddlehub as hub from paddlehub.datasets import ESC50 sr = 44100 # 音频文件的采样率 - wav_file = '/data/cat.wav' # 用于预测的音频文件路径 + wav_file = '/PATH/TO/AUDIO' # 用于预测的音频文件路径 checkpoint = 'model.pdparams' # 用于预测的模型参数 label_map = {idx: label for idx, label in enumerate(ESC50.label_list)} @@ -86,8 +70,8 @@ def predict( print('File: {}\tLable: {}'.format(wav_file, result[0])) ``` -- Audioset Tagging - ```python + - ```python + # Audioset Tagging import librosa import numpy as np import paddlehub as hub @@ -105,7 +89,7 @@ def predict( print(msg) sr = 44100 # 音频文件的采样率 - wav_file = '/data/cat.wav' # 用于预测的音频文件路径 + wav_file = '/PATH/TO/AUDIO' # 用于预测的音频文件路径 label_file = './audioset_labels.txt' # audioset标签文本文件 topk = 10 # 展示的topk数 @@ -130,23 +114,58 @@ def predict( show_topk(topk, label_map, wav_file, result[0]) ``` -详情可参考PaddleHub示例： -- [AudioClassification](https://github.com/PaddlePaddle/PaddleHub/tree/release/v2.0/demo/audio_classification) - - -## 查看代码 - -https://github.com/qiuqiangkong/audioset_tagging_cnn +- ### 2、API + - ```python + def __init__( + task, + num_class=None, + label_map=None, + load_checkpoint=None, + **kwargs, + ) + ``` + - 创建Module对象。 + + - **参数** + - `task`： 任务名称，可为`sound-cls`或者`None`。`sound-cls`代表声音分类任务，可以对声音分类的数据集进行finetune；为`None`时可以获取预训练模型对音频进行分类/Tagging。 + - `num_classes`：声音分类任务的类别数，finetune时需要指定，数值与具体使用的数据集类别数一致。 + - `label_map`：预测时的类别映射表。 + - `load_checkpoint`：使用PaddleHub Fine-tune api训练保存的模型参数文件路径。 + - `**kwargs`：用户额外指定的关键字字典类型的参数。 + + - ```python + def predict( + data, + sample_rate, + batch_size=1, + feat_type='mel', + use_gpu=False + ) + ``` + - 模型预测，输入为音频波形数据，输出为分类标签。 -## 依赖 + - **参数** + - `data`： 待预测数据，格式为\[waveform1, wavwform2…,\]，其中每个元素都是一个一维numpy列表，是音频的波形采样数值列表。 + - `sample_rate`：音频文件的采样率。 + - `feat_type`：音频特征的种类选取，当前支持`'mel'`(详情可查看[Mel-frequency cepstrum](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum))和原始波形特征`'raw'`。 + - `batch_size`：模型批处理大小。 + - `use_gpu`：是否使用gpu，默认为False。对于GPU用户，建议开启use_gpu。 -paddlepaddle >= 2.0.0 + - **返回** + - `results`：list类型，不同任务类型的返回结果如下 + - 声音分类(task参数为`sound-cls`)：列表里包含每个音频文件的分类标签。 + - Tagging(task参数为`None`)：列表里包含每个音频文件527个类别([Audioset标签](https://research.google.com/audioset/))的得分。 -paddlehub >= 2.0.0 + 详情可参考PaddleHub示例： + - [AudioClassification](https://github.com/PaddlePaddle/PaddleHub/tree/release/v2.0/demo/audio_classification) -## 更新历史 +## 四、更新历史 * 1.0.0 初始发布，动态图版本模型，支持声音分类`sound-cls`任务的fine-tune和基于Audioset Tagging预测。 + + ```shell + $ hub install panns_cnn6 + ``` diff --git a/modules/audio/tts/fastspeech2_baker/README.md b/modules/audio/tts/fastspeech2_baker/README.md new file mode 100644 index 00000000..1ec244d6 --- /dev/null +++ b/modules/audio/tts/fastspeech2_baker/README.md @@ -0,0 +1,156 @@ +# fastspeech2_baker + +|模型名称|fastspeech2_baker| +| :--- | :---: | +|类别|语音-语音合成| +|网络|FastSpeech2| +|数据集|Chinese Standard Mandarin Speech Copus| +|是否支持Fine-tuning|否| +|模型大小|621MB| +|最新更新日期|2021-10-20| +|数据指标|-| + +## 一、模型基本信息 + +### 模型介绍 + +FastSpeech2是微软亚洲研究院和微软Azure语音团队联合浙江大学于2020年提出的语音合成(Text to Speech, TTS)模型。FastSpeech2是FastSpeech的改进版，解决了FastSpeech依赖Teacher-Student的知识蒸馏框架，训练流程比较复杂和训练目标相比真实语音存在信息损失的问题。 + +FastSpeech2的模型架构如下图所示，它沿用FastSpeech中提出的Feed-Forward Transformer(FFT)架构，但在音素编码器和梅尔频谱解码器中加入了一个可变信息适配器(Variance Adaptor)，从而支持在FastSpeech2中引入更多语音中变化的信息，例如时长、音高、音量(频谱能量)等，来解决语音合成中的一对多映射问题。 + +

+
+

+ +Parallel WaveGAN是一种使用了无蒸馏的对抗生成网络，快速且占用空间小的波形生成方法。该方法通过联合优化多分辨率谱图和对抗损失函数来训练非自回归WaveNet，可以有效捕获真实语音波形的时频分布。Parallel WaveGAN的结构如下图所示： + +

+
+

+ +fastspeech2_baker使用了FastSpeech2作为声学模型，使用Parallel WaveGAN作为声码器，并在[中文标准女声音库(Chinese Standard Mandarin Speech Copus)](https://www.data-baker.com/open_source.html)数据集上进行了预训练，可直接用于预测合成音频。 + +更多详情请参考: +- [FastSpeech 2: Fast and High-Quality End-to-End Text-to-Speech](https://arxiv.org/abs/2006.04558) +- [FastSpeech语音合成系统技术升级，微软联合浙大提出FastSpeech2](https://www.msra.cn/zh-cn/news/features/fastspeech2) +- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://arxiv.org/abs/1910.11480) + +## 二、安装 + +- ### 1、环境依赖 + + - paddlepaddle >= 2.1.0 + + - paddlehub >= 2.1.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst) + +- ### 2、安装 + + - ```shell + $ hub install fastspeech2_baker + ``` + - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md) + | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md) + + +## 三、模型API预测 + +- ### 1、预测代码示例 + + ```python + import paddlehub as hub + + # 需要合成语音的文本 + sentences = ['这是一段测试语音合成的音频。'] + + model = hub.Module( + name='fastspeech2_baker', + version='1.0.0') + wav_files = model.generate(sentences) + + # 打印合成的音频文件的路径 + print(wav_files) + ``` + + 详情可参考PaddleHub示例： + - [语音合成](../../../../demo/text_to_speech) + + +- ### 2、API + - ```python + def __init__(output_dir) + ``` + + - 创建Module对象（动态图组网版本） + + - **参数** + + - `output_dir`： 合成音频文件的输出目录。 + + - ```python + def generate( + sentences, + device='cpu', + ) + ``` + - 将输入的文本合成为音频文件并保存到输出目录。 + + - **参数** + + - `sentences`：合成音频的文本列表，类型为`List[str]`。 + - `device`：预测时使用的设备，默认为`cpu`，如需使用gpu预测，请设置为`gpu`。 + + - **返回** + + - `wav_files`：`List[str]`类型，返回合成音频的存放路径。 + + +## 四、服务部署 + +- PaddleHub Serving可以部署一个在线的语音识别服务。 + +- ### 第一步：启动PaddleHub Serving + + - ```shell + $ hub serving start -m fastspeech2_baker + ``` + + - 这样就完成了一个语音识别服务化API的部署，默认端口号为8866。 + + - **NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA_VISIBLE_DEVICES环境变量，否则不用设置。 + +- ### 第二步：发送预测请求 + + - 配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果 + + - ```python + import requests + import json + + # 需要合成语音的文本 + sentences = [ + '这是第一段测试语音合成的音频。', + '这是第二段测试语音合成的音频。', + ] + + # 以key的方式指定text传入预测方法的时的参数，此例中为"sentences" + data = {"sentences": sentences} + + # 发送post请求，content-type类型应指定json方式，url中的ip地址需改为对应机器的ip + url = "http://127.0.0.1:8866/predict/fastspeech2_baker" + + # 指定post请求的headers为application/json方式 + headers = {"Content-Type": "application/json"} + + r = requests.post(url=url, headers=headers, data=json.dumps(data)) + print(r.json()) + ``` + +## 五、更新历史 + +* 1.0.0 + + 初始发布 + + ```shell + $ hub install fastspeech2_baker + ``` diff --git a/modules/audio/tts/fastspeech2_baker/__init__.py b/modules/audio/tts/fastspeech2_baker/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/modules/audio/tts/fastspeech2_baker/assets/fastspeech2_nosil_baker_ckpt_0.4/default.yaml b/modules/audio/tts/fastspeech2_baker/assets/fastspeech2_nosil_baker_ckpt_0.4/default.yaml new file mode 100644 index 00000000..63eaef16 --- /dev/null +++ b/modules/audio/tts/fastspeech2_baker/assets/fastspeech2_nosil_baker_ckpt_0.4/default.yaml @@ -0,0 +1,104 @@ +########################################################### +# FEATURE EXTRACTION SETTING # +########################################################### + +fs: 24000 # sr +n_fft: 2048 # FFT size. +n_shift: 300 # Hop size. +win_length: 1200 # Window length. + # If set to null, it will be the same as fft_size. +window: "hann" # Window function. + +# Only used for feats_type != raw + +fmin: 80 # Minimum frequency of Mel basis. +fmax: 7600 # Maximum frequency of Mel basis. +n_mels: 80 # The number of mel basis. + +# Only used for the model using pitch features (e.g. FastSpeech2) +f0min: 80 # Maximum f0 for pitch extraction. +f0max: 400 # Minimum f0 for pitch extraction. + + +########################################################### +# DATA SETTING # +########################################################### +batch_size: 64 +num_workers: 4 + + +########################################################### +# MODEL SETTING # +########################################################### +model: + adim: 384 # attention dimension + aheads: 2 # number of attention heads + elayers: 4 # number of encoder layers + eunits: 1536 # number of encoder ff units + dlayers: 4 # number of decoder layers + dunits: 1536 # number of decoder ff units + positionwise_layer_type: conv1d # type of position-wise layer + positionwise_conv_kernel_size: 3 # kernel size of position wise conv layer + duration_predictor_layers: 2 # number of layers of duration predictor + duration_predictor_chans: 256 # number of channels of duration predictor + duration_predictor_kernel_size: 3 # filter size of duration predictor + postnet_layers: 5 # number of layers of postnset + postnet_filts: 5 # filter size of conv layers in postnet + postnet_chans: 256 # number of channels of conv layers in postnet + use_masking: True # whether to apply masking for padded part in loss calculation + use_scaled_pos_enc: True # whether to use scaled positional encoding + encoder_normalize_before: True # whether to perform layer normalization before the input + decoder_normalize_before: True # whether to perform layer normalization before the input + reduction_factor: 1 # reduction factor + init_type: xavier_uniform # initialization type + init_enc_alpha: 1.0 # initial value of alpha of encoder scaled position encoding + init_dec_alpha: 1.0 # initial value of alpha of decoder scaled position encoding + transformer_enc_dropout_rate: 0.2 # dropout rate for transformer encoder layer + transformer_enc_positional_dropout_rate: 0.2 # dropout rate for transformer encoder positional encoding + transformer_enc_attn_dropout_rate: 0.2 # dropout rate for transformer encoder attention layer + transformer_dec_dropout_rate: 0.2 # dropout rate for transformer decoder layer + transformer_dec_positional_dropout_rate: 0.2 # dropout rate for transformer decoder positional encoding + transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer + pitch_predictor_layers: 5 # number of conv layers in pitch predictor + pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor + pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor + pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor + pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch + pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch + stop_gradient_from_pitch_predictor: true # whether to stop the gradient from pitch predictor to encoder + energy_predictor_layers: 2 # number of conv layers in energy predictor + energy_predictor_chans: 256 # number of channels of conv layers in energy predictor + energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor + energy_predictor_dropout: 0.5 # dropout rate in energy predictor + energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy + energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy + stop_gradient_from_energy_predictor: false # whether to stop the gradient from energy predictor to encoder + + + +########################################################### +# UPDATER SETTING # +########################################################### +updater: + use_masking: True # whether to apply masking for padded part in loss calculation + + + +########################################################### +# OPTIMIZER SETTING # +########################################################### +optimizer: + optim: adam # optimizer type + learning_rate: 0.001 # learning rate + +########################################################### +# TRAINING SETTING # +########################################################### +max_epoch: 1000 +num_snapshots: 5 + + +########################################################### +# OTHER SETTING # +########################################################### +seed: 10086 diff --git a/modules/audio/tts/fastspeech2_baker/assets/fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt b/modules/audio/tts/fastspeech2_baker/assets/fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt new file mode 100644 index 00000000..a7ca3402 --- /dev/null +++ b/modules/audio/tts/fastspeech2_baker/assets/fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt @@ -0,0 +1,268 @@ + 0 + 1 +a1 2 +a2 3 +a3 4 +a4 5 +a5 6 +ai1 7 +ai2 8 +ai3 9 +ai4 10 +ai5 11 +air2 12 +air4 13 +an1 14 +an2 15 +an3 16 +an4 17 +an5 18 +ang1 19 +ang2 20 +ang3 21 +ang4 22 +ang5 23 +anr1 24 +anr3 25 +anr4 26 +ao1 27 +ao2 28 +ao3 29 +ao4 30 +ao5 31 +aor3 32 +aor4 33 +ar2 34 +ar3 35 +ar4 36 +b 37 +c 38 +ch 39 +d 40 +e1 41 +e2 42 +e3 43 +e4 44 +e5 45 +ei1 46 +ei2 47 +ei3 48 +ei4 49 +ei5 50 +en1 51 +en2 52 +en3 53 +en4 54 +en5 55 +eng1 56 +eng2 57 +eng3 58 +eng4 59 +eng5 60 +enr1 61 +enr2 62 +enr4 63 +enr5 64 +er2 65 +er3 66 +er4 67 +er5 68 +f 69 +g 70 +h 71 +i1 72 +i2 73 +i3 74 +i4 75 +i5 76 +ia1 77 +ia2 78 +ia3 79 +ia4 80 +ia5 81 +ian1 82 +ian2 83 +ian3 84 +ian4 85 +ian5 86 +iang1 87 +iang2 88 +iang3 89 +iang4 90 +iang5 91 +iangr4 92 +ianr1 93 +ianr2 94 +ianr3 95 +iao1 96 +iao2 97 +iao3 98 +iao4 99 +iao5 100 +iar1 101 +iar3 102 +ie1 103 +ie2 104 +ie3 105 +ie4 106 +ie5 107 +ii1 108 +ii2 109 +ii3 110 +ii4 111 +ii5 112 +iii1 113 +iii2 114 +iii3 115 +iii4 116 +iii5 117 +iiir4 118 +iir2 119 +in1 120 +in2 121 +in3 122 +in4 123 +in5 124 +ing1 125 +ing2 126 +ing3 127 +ing4 128 +ing5 129 +ingr2 130 +ingr3 131 +inr4 132 +io1 133 +io5 134 +iong1 135 +iong2 136 +iong3 137 +iong4 138 +iong5 139 +iou1 140 +iou2 141 +iou3 142 +iou4 143 +iou5 144 +iour1 145 +ir1 146 +ir2 147 +ir3 148 +ir4 149 +ir5 150 +j 151 +k 152 +l 153 +m 154 +n 155 +o1 156 +o2 157 +o3 158 +o4 159 +o5 160 +ong1 161 +ong2 162 +ong3 163 +ong4 164 +ong5 165 +ongr4 166 +ou1 167 +ou2 168 +ou3 169 +ou4 170 +ou5 171 +our2 172 +p 173 +q 174 +r 175 +s 176 +sh 177 +sil 178 +sp 179 +spl 180 +spn 181 +t 182 +u1 183 +u2 184 +u3 185 +u4 186 +u5 187 +ua1 188 +ua2 189 +ua3 190 +ua4 191 +ua5 192 +uai1 193 +uai2 194 +uai3 195 +uai4 196 +uai5 197 +uair4 198 +uan1 199 +uan2 200 +uan3 201 +uan4 202 +uan5 203 +uang1 204 +uang2 205 +uang3 206 +uang4 207 +uang5 208 +uanr1 209 +uanr2 210 +uei1 211 +uei2 212 +uei3 213 +uei4 214 +uei5 215 +ueir1 216 +ueir3 217 +ueir4 218 +uen1 219 +uen2 220 +uen3 221 +uen4 222 +uen5 223 +ueng1 224 +ueng2 225 +ueng3 226 +ueng4 227 +uenr3 228 +uenr4 229 +uo1 230 +uo2 231 +uo3 232 +uo4 233 +uo5 234 +uor2 235 +uor3 236 +ur3 237 +ur4 238 +v1 239 +v2 240 +v3 241 +v4 242 +v5 243 +van1 244 +van2 245 +van3 246 +van4 247 +van5 248 +vanr4 249 +ve1 250 +ve2 251 +ve3 252 +ve4 253 +ve5 254 +vn1 255 +vn2 256 +vn3 257 +vn4 258 +vn5 259 +x 260 +z 261 +zh 262 +， 263 +。 264 +？ 265 +！ 266 + 267 diff --git a/modules/audio/tts/fastspeech2_baker/assets/pwg_baker_ckpt_0.4/pwg_default.yaml b/modules/audio/tts/fastspeech2_baker/assets/pwg_baker_ckpt_0.4/pwg_default.yaml new file mode 100644 index 00000000..17edbc25 --- /dev/null +++ b/modules/audio/tts/fastspeech2_baker/assets/pwg_baker_ckpt_0.4/pwg_default.yaml @@ -0,0 +1,128 @@ +# This is the hyperparameter configuration file for Parallel WaveGAN. +# Please make sure this is adjusted for the CSMSC dataset. If you want to +# apply to the other dataset, you might need to carefully change some parameters. +# This configuration requires 12 GB GPU memory and takes ~3 days on RTX TITAN. + +########################################################### +# FEATURE EXTRACTION SETTING # +########################################################### +fs: 24000 # Sampling rate. +n_fft: 2048 # FFT size. (in samples) +n_shift: 300 # Hop size. (in samples) +win_length: 1200 # Window length. (in samples) + # If set to null, it will be the same as fft_size. +window: "hann" # Window function. +n_mels: 80 # Number of mel basis. +fmin: 80 # Minimum freq in mel basis calculation. +fmax: 7600 # Maximum frequency in mel basis calculation. +# global_gain_scale: 1.0 # Will be multiplied to all of waveform. +trim_silence: false # Whether to trim the start and end of silence. +top_db: 60 # Need to tune carefully if the recording is not good. +trim_frame_length: 2048 # Frame size in trimming.(in samples) +trim_hop_length: 512 # Hop size in trimming.(in samples) + +########################################################### +# GENERATOR NETWORK ARCHITECTURE SETTING # +########################################################### +generator_params: + in_channels: 1 # Number of input channels. + out_channels: 1 # Number of output channels. + kernel_size: 3 # Kernel size of dilated convolution. + layers: 30 # Number of residual block layers. + stacks: 3 # Number of stacks i.e., dilation cycles. + residual_channels: 64 # Number of channels in residual conv. + gate_channels: 128 # Number of channels in gated conv. + skip_channels: 64 # Number of channels in skip conv. + aux_channels: 80 # Number of channels for auxiliary feature conv. + # Must be the same as num_mels. + aux_context_window: 2 # Context window size for auxiliary feature. + # If set to 2, previous 2 and future 2 frames will be considered. + dropout: 0.0 # Dropout rate. 0.0 means no dropout applied. + bias: true # use bias in residual blocks + use_weight_norm: true # Whether to use weight norm. + # If set to true, it will be applied to all of the conv layers. + use_causal_conv: false # use causal conv in residual blocks and upsample layers + # upsample_net: "ConvInUpsampleNetwork" # Upsampling network architecture. + upsample_scales: [4, 5, 3, 5] # Upsampling scales. Prodcut of these must be the same as hop size. + interpolate_mode: "nearest" # upsample net interpolate mode + freq_axis_kernel_size: 1 # upsamling net: convolution kernel size in frequencey axis + nonlinear_activation: null + nonlinear_activation_params: {} + +########################################################### +# DISCRIMINATOR NETWORK ARCHITECTURE SETTING # +########################################################### +discriminator_params: + in_channels: 1 # Number of input channels. + out_channels: 1 # Number of output channels. + kernel_size: 3 # Number of output channels. + layers: 10 # Number of conv layers. + conv_channels: 64 # Number of chnn layers. + bias: true # Whether to use bias parameter in conv. + use_weight_norm: true # Whether to use weight norm. + # If set to true, it will be applied to all of the conv layers. + nonlinear_activation: "LeakyReLU" # Nonlinear function after each conv. + nonlinear_activation_params: # Nonlinear function parameters + negative_slope: 0.2 # Alpha in LeakyReLU. + +########################################################### +# STFT LOSS SETTING # +########################################################### +stft_loss_params: + fft_sizes: [1024, 2048, 512] # List of FFT size for STFT-based loss. + hop_sizes: [120, 240, 50] # List of hop size for STFT-based loss + win_lengths: [600, 1200, 240] # List of window length for STFT-based loss. + window: "hann" # Window function for STFT-based loss + +########################################################### +# ADVERSARIAL LOSS SETTING # +########################################################### +lambda_adv: 4.0 # Loss balancing coefficient. + +########################################################### +# DATA LOADER SETTING # +########################################################### +batch_size: 6 # Batch size. +batch_max_steps: 25500 # Length of each audio in batch. Make sure dividable by hop_size. +pin_memory: true # Whether to pin memory in Pytorch DataLoader. +num_workers: 4 # Number of workers in Pytorch DataLoader. +remove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps. +allow_cache: true # Whether to allow cache in dataset. If true, it requires cpu memory. + +########################################################### +# OPTIMIZER & SCHEDULER SETTING # +########################################################### +generator_optimizer_params: + epsilon: 1.0e-6 # Generator's epsilon. + weight_decay: 0.0 # Generator's weight decay coefficient. +generator_scheduler_params: + learning_rate: 0.0001 # Generator's learning rate. + step_size: 200000 # Generator's scheduler step size. + gamma: 0.5 # Generator's scheduler gamma. + # At each step size, lr will be multiplied by this parameter. +generator_grad_norm: 10 # Generator's gradient norm. +discriminator_optimizer_params: + epsilon: 1.0e-6 # Discriminator's epsilon. + weight_decay: 0.0 # Discriminator's weight decay coefficient. +discriminator_scheduler_params: + learning_rate: 0.00005 # Discriminator's learning rate. + step_size: 200000 # Discriminator's scheduler step size. + gamma: 0.5 # Discriminator's scheduler gamma. + # At each step size, lr will be multiplied by this parameter. +discriminator_grad_norm: 1 # Discriminator's gradient norm. + +########################################################### +# INTERVAL SETTING # +########################################################### +discriminator_train_start_steps: 100000 # Number of steps to start to train discriminator. +train_max_steps: 400000 # Number of training steps. +save_interval_steps: 5000 # Interval steps to save checkpoint. +eval_interval_steps: 1000 # Interval steps to evaluate the network. + + +########################################################### +# OTHER SETTING # +########################################################### +num_save_intermediate_results: 4 # Number of results to be saved as intermediate results. +num_snapshots: 10 # max number of snapshots to keep while training +seed: 42 # random seed for paddle, random, and np.random diff --git a/modules/audio/tts/fastspeech2_baker/module.py b/modules/audio/tts/fastspeech2_baker/module.py new file mode 100644 index 00000000..03d150c9 --- /dev/null +++ b/modules/audio/tts/fastspeech2_baker/module.py @@ -0,0 +1,125 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from pathlib import Path +from typing import List + +import numpy as np +import paddle +from paddlehub.env import MODULE_HOME +from paddlehub.module.module import moduleinfo, serving +from paddlehub.utils.log import logger +from parakeet.frontend.zh_frontend import Frontend +from parakeet.models.fastspeech2 import FastSpeech2 +from parakeet.models.fastspeech2 import FastSpeech2Inference +from parakeet.models.parallel_wavegan import PWGGenerator +from parakeet.models.parallel_wavegan import PWGInference +from parakeet.modules.normalizer import ZScore +import soundfile as sf +from yacs.config import CfgNode +import yaml + + +@moduleinfo(name="fastspeech2_baker", version="1.0.0", summary="", author="Baidu", author_email="", type="audio/tts") +class FastSpeech(paddle.nn.Layer): + def __init__(self, output_dir='./wavs'): + super(FastSpeech, self).__init__() + fastspeech2_res_dir = os.path.join(MODULE_HOME, 'fastspeech2_baker', 'assets/fastspeech2_nosil_baker_ckpt_0.4') + pwg_res_dir = os.path.join(MODULE_HOME, 'fastspeech2_baker', 'assets/pwg_baker_ckpt_0.4') + + phones_dict = os.path.join(fastspeech2_res_dir, 'phone_id_map.txt') + with open(phones_dict, "r") as f: + phn_id = [line.strip().split() for line in f.readlines()] + vocab_size = len(phn_id) + + # fastspeech2 + fastspeech2_config = os.path.join(fastspeech2_res_dir, 'default.yaml') + with open(fastspeech2_config) as f: + fastspeech2_config = CfgNode(yaml.safe_load(f)) + self.samplerate = fastspeech2_config.fs + + fastspeech2_checkpoint = os.path.join(fastspeech2_res_dir, 'snapshot_iter_76000.pdz') + model = FastSpeech2(idim=vocab_size, odim=fastspeech2_config.n_mels, **fastspeech2_config["model"]) + model.set_state_dict(paddle.load(fastspeech2_checkpoint)["main_params"]) + logger.info('Load fastspeech2 params from %s' % os.path.abspath(fastspeech2_checkpoint)) + model.eval() + + # vocoder + pwg_config = os.path.join(pwg_res_dir, 'pwg_default.yaml') + with open(pwg_config) as f: + pwg_config = CfgNode(yaml.safe_load(f)) + + pwg_checkpoint = os.path.join(pwg_res_dir, 'pwg_snapshot_iter_400000.pdz') + vocoder = PWGGenerator(**pwg_config["generator_params"]) + vocoder.set_state_dict(paddle.load(pwg_checkpoint)["generator_params"]) + logger.info('Load vocoder params from %s' % os.path.abspath(pwg_checkpoint)) + vocoder.remove_weight_norm() + vocoder.eval() + + # frontend + self.frontend = Frontend(phone_vocab_path=phones_dict) + + # stat + fastspeech2_stat = os.path.join(fastspeech2_res_dir, 'speech_stats.npy') + stat = np.load(fastspeech2_stat) + mu, std = stat + mu = paddle.to_tensor(mu) + std = paddle.to_tensor(std) + fastspeech2_normalizer = ZScore(mu, std) + + pwg_stat = os.path.join(pwg_res_dir, 'pwg_stats.npy') + stat = np.load(pwg_stat) + mu, std = stat + mu = paddle.to_tensor(mu) + std = paddle.to_tensor(std) + pwg_normalizer = ZScore(mu, std) + + # inference + self.fastspeech2_inference = FastSpeech2Inference(fastspeech2_normalizer, model) + self.pwg_inference = PWGInference(pwg_normalizer, vocoder) + + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + + def forward(self, text: str): + wav = None + input_ids = self.frontend.get_input_ids(text, merge_sentences=True) + phone_ids = input_ids["phone_ids"] + for part_phone_ids in phone_ids: + with paddle.no_grad(): + mel = self.fastspeech2_inference(part_phone_ids) + temp_wav = self.pwg_inference(mel) + if wav is None: + wav = temp_wav + else: + wav = paddle.concat([wav, temp_wav]) + + return wav + + @serving + def generate(self, sentences: List[str], device='cpu'): + assert isinstance(sentences, list) and isinstance(sentences[0], str), \ + 'Input data should be List[str], but got {}'.format(type(sentences)) + + paddle.set_device(device) + wav_files = [] + for i, sentence in enumerate(sentences): + wav = self(sentence) + wav_file = str(self.output_dir.absolute() / (str(i + 1) + ".wav")) + sf.write(wav_file, wav.numpy(), samplerate=self.samplerate) + wav_files.append(wav_file) + + logger.info('{} wave files have been generated in {}'.format(len(sentences), self.output_dir.absolute())) + return wav_files diff --git a/modules/audio/tts/fastspeech2_baker/requirements.txt b/modules/audio/tts/fastspeech2_baker/requirements.txt new file mode 100644 index 00000000..f410f4f4 --- /dev/null +++ b/modules/audio/tts/fastspeech2_baker/requirements.txt @@ -0,0 +1 @@ +git+https://github.com/PaddlePaddle/Parakeet@8040cb0#egg=paddle-parakeet diff --git a/modules/audio/tts/fastspeech2_ljspeech/README.md b/modules/audio/tts/fastspeech2_ljspeech/README.md new file mode 100644 index 00000000..54329460 --- /dev/null +++ b/modules/audio/tts/fastspeech2_ljspeech/README.md @@ -0,0 +1,156 @@ +# fastspeech2_ljspeech + +|模型名称|fastspeech2_ljspeech| +| :--- | :---: | +|类别|语音-语音合成| +|网络|FastSpeech2| +|数据集|LJSpeech-1.1| +|是否支持Fine-tuning|否| +|模型大小|425MB| +|最新更新日期|2021-10-20| +|数据指标|-| + +## 一、模型基本信息 + +### 模型介绍 + +FastSpeech2是微软亚洲研究院和微软Azure语音团队联合浙江大学于2020年提出的语音合成(Text to Speech, TTS)模型。FastSpeech2是FastSpeech的改进版，解决了FastSpeech依赖Teacher-Student的知识蒸馏框架，训练流程比较复杂和训练目标相比真实语音存在信息损失的问题。 + +FastSpeech2的模型架构如下图所示，它沿用FastSpeech中提出的Feed-Forward Transformer(FFT)架构，但在音素编码器和梅尔频谱解码器中加入了一个可变信息适配器(Variance Adaptor)，从而支持在FastSpeech2中引入更多语音中变化的信息，例如时长、音高、音量(频谱能量)等，来解决语音合成中的一对多映射问题。 + +

+
+

+ +Parallel WaveGAN是一种使用了无蒸馏的对抗生成网络，快速且占用空间小的波形生成方法。该方法通过联合优化多分辨率谱图和对抗损失函数来训练非自回归WaveNet，可以有效捕获真实语音波形的时频分布。Parallel WaveGAN的结构如下图所示： + +

+
+

+ +fastspeech2_ljspeech使用了FastSpeech2作为声学模型，使用Parallel WaveGAN作为声码器，并在[The LJ Speech Dataset](https://keithito.com/LJ-Speech-Dataset/)数据集上进行了预训练，可直接用于预测合成音频。 + +更多详情请参考: +- [FastSpeech 2: Fast and High-Quality End-to-End Text-to-Speech](https://arxiv.org/abs/2006.04558) +- [FastSpeech语音合成系统技术升级，微软联合浙大提出FastSpeech2](https://www.msra.cn/zh-cn/news/features/fastspeech2) +- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://arxiv.org/abs/1910.11480) + +## 二、安装 + +- ### 1、环境依赖 + + - paddlepaddle >= 2.1.0 + + - paddlehub >= 2.1.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst) + +- ### 2、安装 + + - ```shell + $ hub install fastspeech2_ljspeech + ``` + - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md) + | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md) + + +## 三、模型API预测 + +- ### 1、预测代码示例 + + ```python + import paddlehub as hub + + # 需要合成语音的文本 + sentences = ['The quick brown fox jumps over a lazy dog.'] + + model = hub.Module( + name='fastspeech2_ljspeech', + version='1.0.0') + wav_files = model.generate(sentences) + + # 打印合成的音频文件的路径 + print(wav_files) + ``` + + 详情可参考PaddleHub示例： + - [语音合成](../../../../demo/text_to_speech) + + +- ### 2、API + - ```python + def __init__(output_dir) + ``` + + - 创建Module对象（动态图组网版本） + + - **参数** + + - `output_dir`： 合成音频文件的输出目录。 + + - ```python + def generate( + sentences, + device='cpu', + ) + ``` + - 将输入的文本合成为音频文件并保存到输出目录。 + + - **参数** + + - `sentences`：合成音频的文本列表，类型为`List[str]`。 + - `device`：预测时使用的设备，默认为`cpu`，如需使用gpu预测，请设置为`gpu`。 + + - **返回** + + - `wav_files`：`List[str]`类型，返回合成音频的存放路径。 + + +## 四、服务部署 + +- PaddleHub Serving可以部署一个在线的语音识别服务。 + +- ### 第一步：启动PaddleHub Serving + + - ```shell + $ hub serving start -m fastspeech2_ljspeech + ``` + + - 这样就完成了一个语音识别服务化API的部署，默认端口号为8866。 + + - **NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA_VISIBLE_DEVICES环境变量，否则不用设置。 + +- ### 第二步：发送预测请求 + + - 配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果 + + - ```python + import requests + import json + + # 需要合成语音的文本 + sentences = [ + 'The quick brown fox jumps over a lazy dog.', + 'Today is a good day!', + ] + + # 以key的方式指定text传入预测方法的时的参数，此例中为"sentences" + data = {"sentences": sentences} + + # 发送post请求，content-type类型应指定json方式，url中的ip地址需改为对应机器的ip + url = "http://127.0.0.1:8866/predict/fastspeech2_ljspeech" + + # 指定post请求的headers为application/json方式 + headers = {"Content-Type": "application/json"} + + r = requests.post(url=url, headers=headers, data=json.dumps(data)) + print(r.json()) + ``` + +## 五、更新历史 + +* 1.0.0 + + 初始发布 + + ```shell + $ hub install fastspeech2_ljspeech + ``` diff --git a/modules/audio/tts/fastspeech2_ljspeech/__init__.py b/modules/audio/tts/fastspeech2_ljspeech/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/modules/audio/tts/fastspeech2_ljspeech/assets/fastspeech2_nosil_ljspeech_ckpt_0.5/default.yaml b/modules/audio/tts/fastspeech2_ljspeech/assets/fastspeech2_nosil_ljspeech_ckpt_0.5/default.yaml new file mode 100644 index 00000000..cabcca80 --- /dev/null +++ b/modules/audio/tts/fastspeech2_ljspeech/assets/fastspeech2_nosil_ljspeech_ckpt_0.5/default.yaml @@ -0,0 +1,104 @@ +########################################################### +# FEATURE EXTRACTION SETTING # +########################################################### + +fs: 22050 # sr +n_fft: 1024 # FFT size. +n_shift: 256 # Hop size. +win_length: null # Window length. + # If set to null, it will be the same as fft_size. +window: "hann" # Window function. + +# Only used for feats_type != raw + +fmin: 80 # Minimum frequency of Mel basis. +fmax: 7600 # Maximum frequency of Mel basis. +n_mels: 80 # The number of mel basis. + +# Only used for the model using pitch features (e.g. FastSpeech2) +f0min: 80 # Maximum f0 for pitch extraction. +f0max: 400 # Minimum f0 for pitch extraction. + + +########################################################### +# DATA SETTING # +########################################################### +batch_size: 64 +num_workers: 4 + + +########################################################### +# MODEL SETTING # +########################################################### +model: + adim: 384 # attention dimension + aheads: 2 # number of attention heads + elayers: 4 # number of encoder layers + eunits: 1536 # number of encoder ff units + dlayers: 4 # number of decoder layers + dunits: 1536 # number of decoder ff units + positionwise_layer_type: conv1d # type of position-wise layer + positionwise_conv_kernel_size: 3 # kernel size of position wise conv layer + duration_predictor_layers: 2 # number of layers of duration predictor + duration_predictor_chans: 256 # number of channels of duration predictor + duration_predictor_kernel_size: 3 # filter size of duration predictor + postnet_layers: 5 # number of layers of postnset + postnet_filts: 5 # filter size of conv layers in postnet + postnet_chans: 256 # number of channels of conv layers in postnet + use_masking: True # whether to apply masking for padded part in loss calculation + use_scaled_pos_enc: True # whether to use scaled positional encoding + encoder_normalize_before: True # whether to perform layer normalization before the input + decoder_normalize_before: True # whether to perform layer normalization before the input + reduction_factor: 1 # reduction factor + init_type: xavier_uniform # initialization type + init_enc_alpha: 1.0 # initial value of alpha of encoder scaled position encoding + init_dec_alpha: 1.0 # initial value of alpha of decoder scaled position encoding + transformer_enc_dropout_rate: 0.2 # dropout rate for transformer encoder layer + transformer_enc_positional_dropout_rate: 0.2 # dropout rate for transformer encoder positional encoding + transformer_enc_attn_dropout_rate: 0.2 # dropout rate for transformer encoder attention layer + transformer_dec_dropout_rate: 0.2 # dropout rate for transformer decoder layer + transformer_dec_positional_dropout_rate: 0.2 # dropout rate for transformer decoder positional encoding + transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer + pitch_predictor_layers: 5 # number of conv layers in pitch predictor + pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor + pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor + pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor + pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch + pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch + stop_gradient_from_pitch_predictor: true # whether to stop the gradient from pitch predictor to encoder + energy_predictor_layers: 2 # number of conv layers in energy predictor + energy_predictor_chans: 256 # number of channels of conv layers in energy predictor + energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor + energy_predictor_dropout: 0.5 # dropout rate in energy predictor + energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy + energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy + stop_gradient_from_energy_predictor: false # whether to stop the gradient from energy predictor to encoder + + + +########################################################### +# UPDATER SETTING # +########################################################### +updater: + use_masking: True # whether to apply masking for padded part in loss calculation + + + +########################################################### +# OPTIMIZER SETTING # +########################################################### +optimizer: + optim: adam # optimizer type + learning_rate: 0.001 # learning rate + +########################################################### +# TRAINING SETTING # +########################################################### +max_epoch: 1000 +num_snapshots: 5 + + +########################################################### +# OTHER SETTING # +########################################################### +seed: 10086 diff --git a/modules/audio/tts/fastspeech2_ljspeech/assets/fastspeech2_nosil_ljspeech_ckpt_0.5/phone_id_map.txt b/modules/audio/tts/fastspeech2_ljspeech/assets/fastspeech2_nosil_ljspeech_ckpt_0.5/phone_id_map.txt new file mode 100644 index 00000000..c840e98e --- /dev/null +++ b/modules/audio/tts/fastspeech2_ljspeech/assets/fastspeech2_nosil_ljspeech_ckpt_0.5/phone_id_map.txt @@ -0,0 +1,80 @@ + 0 + 1 +AA0 2 +AA1 3 +AA2 4 +AE0 5 +AE1 6 +AE2 7 +AH0 8 +AH1 9 +AH2 10 +AO0 11 +AO1 12 +AO2 13 +AW0 14 +AW1 15 +AW2 16 +AY0 17 +AY1 18 +AY2 19 +B 20 +CH 21 +D 22 +DH 23 +EH0 24 +EH1 25 +EH2 26 +ER0 27 +ER1 28 +ER2 29 +EY0 30 +EY1 31 +EY2 32 +F 33 +G 34 +HH 35 +IH0 36 +IH1 37 +IH2 38 +IY0 39 +IY1 40 +IY2 41 +JH 42 +K 43 +L 44 +M 45 +N 46 +NG 47 +OW0 48 +OW1 49 +OW2 50 +OY0 51 +OY1 52 +OY2 53 +P 54 +R 55 +S 56 +SH 57 +T 58 +TH 59 +UH0 60 +UH1 61 +UH2 62 +UW0 63 +UW1 64 +UW2 65 +V 66 +W 67 +Y 68 +Z 69 +ZH 70 +sil 71 +sp 72 +spl 73 +spn 74 +, 75 +. 76 +? 77 +! 78 + 79 diff --git a/modules/audio/tts/fastspeech2_ljspeech/assets/pwg_ljspeech_ckpt_0.5/pwg_default.yaml b/modules/audio/tts/fastspeech2_ljspeech/assets/pwg_ljspeech_ckpt_0.5/pwg_default.yaml new file mode 100644 index 00000000..049ab93d --- /dev/null +++ b/modules/audio/tts/fastspeech2_ljspeech/assets/pwg_ljspeech_ckpt_0.5/pwg_default.yaml @@ -0,0 +1,119 @@ +# This is the hyperparameter configuration file for Parallel WaveGAN. +# Please make sure this is adjusted for the LJSpeech dataset. If you want to +# apply to the other dataset, you might need to carefully change some parameters. +# This configuration requires 12 GB GPU memory and takes ~3 days on TITAN V. + +########################################################### +# FEATURE EXTRACTION SETTING # +########################################################### +fs: 22050 # Sampling rate. +n_fft: 1024 # FFT size. (in samples) +n_shift: 256 # Hop size. (in samples) +win_length: null # Window length. (in samples) + # If set to null, it will be the same as fft_size. +window: "hann" # Window function. +n_mels: 80 # Number of mel basis. +fmin: 80 # Minimum freq in mel basis calculation. (Hz) +fmax: 7600 # Maximum frequency in mel basis calculation. (Hz) +trim_silence: false # Whether to trim the start and end of silence. +top_db: 60 # Need to tune carefully if the recording is not good. +trim_frame_length: 2048 # Frame size in trimming. (in samples) +trim_hop_length: 512 # Hop size in trimming. (in samples) + +########################################################### +# GENERATOR NETWORK ARCHITECTURE SETTING # +########################################################### +generator_params: + in_channels: 1 # Number of input channels. + out_channels: 1 # Number of output channels. + kernel_size: 3 # Kernel size of dilated convolution. + layers: 30 # Number of residual block layers. + stacks: 3 # Number of stacks i.e., dilation cycles. + residual_channels: 64 # Number of channels in residual conv. + gate_channels: 128 # Number of channels in gated conv. + skip_channels: 64 # Number of channels in skip conv. + aux_channels: 80 # Number of channels for auxiliary feature conv. + # Must be the same as num_mels. + aux_context_window: 2 # Context window size for auxiliary feature. + # If set to 2, previous 2 and future 2 frames will be considered. + dropout: 0.0 # Dropout rate. 0.0 means no dropout applied. + use_weight_norm: true # Whether to use weight norm. + # If set to true, it will be applied to all of the conv layers. + upsample_scales: [4, 4, 4, 4] # Upsampling scales. Prodcut of these must be the same as hop size. + +########################################################### +# DISCRIMINATOR NETWORK ARCHITECTURE SETTING # +########################################################### +discriminator_params: + in_channels: 1 # Number of input channels. + out_channels: 1 # Number of output channels. + kernel_size: 3 # Number of output channels. + layers: 10 # Number of conv layers. + conv_channels: 64 # Number of chnn layers. + bias: true # Whether to use bias parameter in conv. + use_weight_norm: true # Whether to use weight norm. + # If set to true, it will be applied to all of the conv layers. + nonlinear_activation: "LeakyReLU" # Nonlinear function after each conv. + nonlinear_activation_params: # Nonlinear function parameters + negative_slope: 0.2 # Alpha in LeakyReLU. + +########################################################### +# STFT LOSS SETTING # +########################################################### +stft_loss_params: + fft_sizes: [1024, 2048, 512] # List of FFT size for STFT-based loss. + hop_sizes: [120, 240, 50] # List of hop size for STFT-based loss + win_lengths: [600, 1200, 240] # List of window length for STFT-based loss. + window: "hann" # Window function for STFT-based loss + +########################################################### +# ADVERSARIAL LOSS SETTING # +########################################################### +lambda_adv: 4.0 # Loss balancing coefficient. + +########################################################### +# DATA LOADER SETTING # +########################################################### +batch_size: 8 # Batch size. +batch_max_steps: 25600 # Length of each audio in batch. Make sure dividable by hop_size. +pin_memory: true # Whether to pin memory in Pytorch DataLoader. +num_workers: 4 # Number of workers in Pytorch DataLoader. +remove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps. +allow_cache: true # Whether to allow cache in dataset. If true, it requires cpu memory. + +########################################################### +# OPTIMIZER & SCHEDULER SETTING # +########################################################### +generator_optimizer_params: + epsilon: 1.0e-6 # Generator's epsilon. + weight_decay: 0.0 # Generator's weight decay coefficient. +generator_scheduler_params: + learning_rate: 0.0001 # Generator's learning rate. + step_size: 200000 # Generator's scheduler step size. + gamma: 0.5 # Generator's scheduler gamma. + # At each step size, lr will be multiplied by this parameter. +generator_grad_norm: 10 # Generator's gradient norm. +discriminator_optimizer_params: + epsilon: 1.0e-6 # Discriminator's epsilon. + weight_decay: 0.0 # Discriminator's weight decay coefficient. +discriminator_scheduler_params: + learning_rate: 0.00005 # Discriminator's learning rate. + step_size: 200000 # Discriminator's scheduler step size. + gamma: 0.5 # Discriminator's scheduler gamma. + # At each step size, lr will be multiplied by this parameter. +discriminator_grad_norm: 1 # Discriminator's gradient norm. + +########################################################### +# INTERVAL SETTING # +########################################################### +discriminator_train_start_steps: 100000 # Number of steps to start to train discriminator. +train_max_steps: 400000 # Number of training steps. +save_interval_steps: 5000 # Interval steps to save checkpoint. +eval_interval_steps: 1000 # Interval steps to evaluate the network. + +########################################################### +# OTHER SETTING # +########################################################### +num_save_intermediate_results: 4 # Number of results to be saved as intermediate results. +num_snapshots: 10 # max number of snapshots to keep while training +seed: 42 # random seed for paddle, random, and np.random diff --git a/modules/audio/tts/fastspeech2_ljspeech/module.py b/modules/audio/tts/fastspeech2_ljspeech/module.py new file mode 100644 index 00000000..7281e181 --- /dev/null +++ b/modules/audio/tts/fastspeech2_ljspeech/module.py @@ -0,0 +1,130 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from pathlib import Path +from typing import List + +import numpy as np +import paddle +from paddlehub.env import MODULE_HOME +from paddlehub.module.module import moduleinfo, serving +from paddlehub.utils.log import logger +from parakeet.frontend import English +from parakeet.models.fastspeech2 import FastSpeech2 +from parakeet.models.fastspeech2 import FastSpeech2Inference +from parakeet.models.parallel_wavegan import PWGGenerator +from parakeet.models.parallel_wavegan import PWGInference +from parakeet.modules.normalizer import ZScore +import soundfile as sf +from yacs.config import CfgNode +import yaml + + +@moduleinfo(name="fastspeech2_ljspeech", version="1.0.0", summary="", author="Baidu", author_email="", type="audio/tts") +class FastSpeech(paddle.nn.Layer): + def __init__(self, output_dir='./wavs'): + super(FastSpeech, self).__init__() + fastspeech2_res_dir = os.path.join(MODULE_HOME, 'fastspeech2_ljspeech', + 'assets/fastspeech2_nosil_ljspeech_ckpt_0.5') + pwg_res_dir = os.path.join(MODULE_HOME, 'fastspeech2_ljspeech', 'assets/pwg_ljspeech_ckpt_0.5') + + phones_dict = os.path.join(fastspeech2_res_dir, 'phone_id_map.txt') + with open(phones_dict, "r") as f: + phn_id = [line.strip().split() for line in f.readlines()] + vocab_size = len(phn_id) + self.phone_id_map = {} + for phn, _id in phn_id: + self.phone_id_map[phn] = int(_id) + + # fastspeech2 + fastspeech2_config = os.path.join(fastspeech2_res_dir, 'default.yaml') + with open(fastspeech2_config) as f: + fastspeech2_config = CfgNode(yaml.safe_load(f)) + self.samplerate = fastspeech2_config.fs + + fastspeech2_checkpoint = os.path.join(fastspeech2_res_dir, 'snapshot_iter_100000.pdz') + model = FastSpeech2(idim=vocab_size, odim=fastspeech2_config.n_mels, **fastspeech2_config["model"]) + model.set_state_dict(paddle.load(fastspeech2_checkpoint)["main_params"]) + logger.info('Load fastspeech2 params from %s' % os.path.abspath(fastspeech2_checkpoint)) + model.eval() + + # vocoder + pwg_config = os.path.join(pwg_res_dir, 'pwg_default.yaml') + with open(pwg_config) as f: + pwg_config = CfgNode(yaml.safe_load(f)) + + pwg_checkpoint = os.path.join(pwg_res_dir, 'pwg_snapshot_iter_400000.pdz') + vocoder = PWGGenerator(**pwg_config["generator_params"]) + vocoder.set_state_dict(paddle.load(pwg_checkpoint)["generator_params"]) + logger.info('Load vocoder params from %s' % os.path.abspath(pwg_checkpoint)) + vocoder.remove_weight_norm() + vocoder.eval() + + # frontend + self.frontend = English() + self.punc = "：，；。？！“”‘’':,;.?!" + + # stat + fastspeech2_stat = os.path.join(fastspeech2_res_dir, 'speech_stats.npy') + stat = np.load(fastspeech2_stat) + mu, std = stat + mu = paddle.to_tensor(mu) + std = paddle.to_tensor(std) + fastspeech2_normalizer = ZScore(mu, std) + + pwg_stat = os.path.join(pwg_res_dir, 'pwg_stats.npy') + stat = np.load(pwg_stat) + mu, std = stat + mu = paddle.to_tensor(mu) + std = paddle.to_tensor(std) + pwg_normalizer = ZScore(mu, std) + + # inference + self.fastspeech2_inference = FastSpeech2Inference(fastspeech2_normalizer, model) + self.pwg_inference = PWGInference(pwg_normalizer, vocoder) + + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + + def forward(self, text: str): + phones = self.frontend.phoneticize(text) + # remove start_symbol and end_symbol + phones = phones[1:-1] + phones = [phn for phn in phones if not phn.isspace()] + phones = [phn if (phn in self.phone_id_map and phn not in self.punc) else "sp" for phn in phones] + phone_ids = [self.phone_id_map[phn] for phn in phones] + phone_ids = paddle.to_tensor(phone_ids) + + with paddle.no_grad(): + mel = self.fastspeech2_inference(phone_ids) + wav = self.pwg_inference(mel) + + return wav + + @serving + def generate(self, sentences: List[str], device='cpu'): + assert isinstance(sentences, list) and isinstance(sentences[0], str), \ + 'Input data should be List[str], but got {}'.format(type(sentences)) + + paddle.set_device(device) + wav_files = [] + for i, sentence in enumerate(sentences): + wav = self(sentence) + wav_file = str(self.output_dir.absolute() / (str(i + 1) + ".wav")) + sf.write(wav_file, wav.numpy(), samplerate=self.samplerate) + wav_files.append(wav_file) + + logger.info('{} wave files have been generated in {}'.format(len(sentences), self.output_dir.absolute())) + return wav_files diff --git a/modules/audio/tts/fastspeech2_ljspeech/requirements.txt b/modules/audio/tts/fastspeech2_ljspeech/requirements.txt new file mode 100644 index 00000000..f410f4f4 --- /dev/null +++ b/modules/audio/tts/fastspeech2_ljspeech/requirements.txt @@ -0,0 +1 @@ +git+https://github.com/PaddlePaddle/Parakeet@8040cb0#egg=paddle-parakeet -- GitLab