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DeepSpeech

PaddlePaddle / DeepSpeech
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D
DeepSpeech
提交 d4ee5916 编写于 作者: G gongel
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Merge branch 'develop' of https://github.com/PaddlePaddle/PaddleSpeech into ted_en_zh_t0

上级 7cef93a6 880e897c
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README.md
浏览文件 @ d4ee5916
......@@ -128,9 +128,9 @@ For **Text-To-Speech**, try pretrained FastSpeech2 + Parallel WaveGAN on CSMSC:
```shell
cd examples/csmsc/tts3
# download the pretrained models and unaip them
wget https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip
wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip
unzip pwg_baker_ckpt_0.4.zip
wget https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_baker_ckpt_0.4.zip
wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip
unzip fastspeech2_nosil_baker_ckpt_0.4.zip
# source the environment
source path.sh
......
demos/metaverse/run.sh
浏览文件 @ d4ee5916
......@@ -25,9 +25,9 @@ fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# download pretrained tts models and unzip
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip
unzip -d download download/pwg_baker_ckpt_0.4.zip
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_baker_ckpt_0.4.zip
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip
unzip -d download download/fastspeech2_nosil_baker_ckpt_0.4.zip
fi
......
demos/story_talker/run.sh
浏览文件 @ d4ee5916
......@@ -19,9 +19,9 @@ fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# download pretrained tts models and unzip
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip
unzip -d download download/pwg_baker_ckpt_0.4.zip
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_baker_ckpt_0.4.zip
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip
unzip -d download download/fastspeech2_nosil_baker_ckpt_0.4.zip
fi
......
demos/style_fs2/run.sh
浏览文件 @ d4ee5916
......@@ -14,9 +14,9 @@ mkdir -p download
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# download pretrained tts models and unzip
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip
unzip -d download download/pwg_baker_ckpt_0.4.zip
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_baker_ckpt_0.4.zip
wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip
unzip -d download download/fastspeech2_nosil_baker_ckpt_0.4.zip
fi
......
docs/source/released_model.md
浏览文件 @ d4ee5916
# Released Models
## Speech-to-Text Models
......@@ -32,27 +31,28 @@ Language Model | Training Data | Token-based | Size | Descriptions
### Acoustic Models
Model Type | Dataset| Example Link | Pretrained Models|Static Models|Siize(static)
:-------------:| :------------:| :-----: | :-----:| :-----:| :-----:
Tacotron2|LJSpeech|[tacotron2-vctk](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts0)|[tacotron2_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_ljspeech_ckpt_0.3.zip)|||
TransformerTTS| LJSpeech| [transformer-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts1)|[transformer_tts_ljspeech_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/transformer_tts_ljspeech_ckpt_0.4.zip)|||
SpeedySpeech| CSMSC | [speedyspeech-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts2) |[speedyspeech_nosil_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/speedyspeech_nosil_baker_ckpt_0.5.zip)|[speedyspeech_nosil_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/speedyspeech_nosil_baker_static_0.5.zip)|12MB|
FastSpeech2| CSMSC |[fastspeech2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts3)|[fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_baker_ckpt_0.4.zip)|[fastspeech2_nosil_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_baker_static_0.4.zip)|157MB|
FastSpeech2| AISHELL-3 |[fastspeech2-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/tts3)|[fastspeech2_nosil_aishell3_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_aishell3_ckpt_0.4.zip)|||
FastSpeech2| LJSpeech |[fastspeech2-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts3)|[fastspeech2_nosil_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_ljspeech_ckpt_0.5.zip)|||
FastSpeech2| VCTK |[fastspeech2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/tts3)|[fastspeech2_nosil_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_vctk_ckpt_0.5.zip)|||
Tacotron2|LJSpeech|[tacotron2-vctk](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts0)|[tacotron2_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.3.zip)|||
TransformerTTS| LJSpeech| [transformer-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts1)|[transformer_tts_ljspeech_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/transformer_tts/transformer_tts_ljspeech_ckpt_0.4.zip)|||
SpeedySpeech| CSMSC | [speedyspeech-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts2) |[speedyspeech_nosil_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_ckpt_0.5.zip)|[speedyspeech_nosil_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_static_0.5.zip)|12MB|
FastSpeech2| CSMSC |[fastspeech2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/tts3)|[fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip)|[fastspeech2_nosil_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_static_0.4.zip)|157MB|
FastSpeech2| AISHELL-3 |[fastspeech2-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/tts3)|[fastspeech2_nosil_aishell3_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_ckpt_0.4.zip)|||
FastSpeech2| LJSpeech |[fastspeech2-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts3)|[fastspeech2_nosil_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_ljspeech_ckpt_0.5.zip)|||
FastSpeech2| VCTK |[fastspeech2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/tts3)|[fastspeech2_nosil_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_vctk_ckpt_0.5.zip)|||
### Vocoders
Model Type | Dataset| Example Link | Pretrained Models| Static Models|Size(static)
:-------------:| :------------:| :-----: | :-----:| :-----:| :-----:
WaveFlow| LJSpeech |[waveflow-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc0)|[waveflow_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/waveflow_ljspeech_ckpt_0.3.zip)|||
Parallel WaveGAN| CSMSC |[PWGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc1)|[pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip)|[pwg_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_static_0.4.zip)|5.1MB|
Parallel WaveGAN| LJSpeech |[PWGAN-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc1)|[pwg_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_ljspeech_ckpt_0.5.zip)|||
Parallel WaveGAN|AISHELL-3 |[PWGAN-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc1)|[pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_aishell3_ckpt_0.5.zip)|||
Parallel WaveGAN| VCTK |[PWGAN-vctk](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/voc1)|[pwg_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_vctk_ckpt_0.5.zip)|||
|Multi Band MelGAN |CSMSC|[MB MelGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc3) | [mb_melgan_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/mb_melgan_baker_ckpt_0.5.zip)|[mb_melgan_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/mb_melgan_baker_static_0.5.zip) |8.2MB|
WaveFlow| LJSpeech |[waveflow-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc0)|[waveflow_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/waveflow/waveflow_ljspeech_ckpt_0.3.zip)|||
Parallel WaveGAN| CSMSC |[PWGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc1)|[pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip)|[pwg_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_static_0.4.zip)|5.1MB|
Parallel WaveGAN| LJSpeech |[PWGAN-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc1)|[pwg_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip)|||
Parallel WaveGAN|AISHELL-3 |[PWGAN-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc1)|[pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip)|||
Parallel WaveGAN| VCTK |[PWGAN-vctk](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/voc1)|[pwg_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.5.zip)|||
|Multi Band MelGAN |CSMSC|[MB MelGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc3) | [mb_melgan_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_ckpt_0.5.zip) <br>[mb_melgan_baker_finetune_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_finetune_ckpt_0.5.zip)|[mb_melgan_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_static_0.5.zip) |8.2MB|
### Voice Cloning
Model Type | Dataset| Example Link | Pretrained Models
:-------------:| :------------:| :-----: | :-----:
GE2E| AISHELL-3, etc. |[ge2e](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/ge2e)|[ge2e_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/ge2e_ckpt_0.3.zip)
GE2E + Tactron2| AISHELL-3 |[ge2e-tactron2-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/vc0)|[tacotron2_aishell3_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_aishell3_ckpt_0.3.zip)
GE2E| AISHELL-3, etc. |[ge2e](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/ge2e)|[ge2e_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/ge2e/ge2e_ckpt_0.3.zip)
GE2E + Tactron2| AISHELL-3 |[ge2e-tactron2-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/vc0)|[tacotron2_aishell3_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_aishell3_ckpt_0.3.zip)
GE2E + FastSpeech2 | AISHELL-3 |[ge2e-fastspeech2-aishell3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/vc1)|[fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip)
docs/source/tts/demo.rst
浏览文件 @ d4ee5916
......@@ -52,7 +52,7 @@ Audio samples generated from ground-truth spectrograms with a vocoder.
<td>
<audio controls="controls">
<source
src="https://paddlespeech.bj.bcebos.com/Parakeet/waveflow_res128_ljspeech_samples_1.0/step_2000k_sentence_0.wav"
src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/waveflow_res128_ljspeech_samples_1.0/step_2000k_sentence_0.wav"
type="audio/wav">
Your browser does not support the <code>audio</code> element.
</audio>
......@@ -72,7 +72,7 @@ Audio samples generated from ground-truth spectrograms with a vocoder.
<td>
<audio controls="controls">
<source
src="https://paddlespeech.bj.bcebos.com/Parakeet/waveflow_res128_ljspeech_samples_1.0/step_2000k_sentence_1.wav"
src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/waveflow_res128_ljspeech_samples_1.0/step_2000k_sentence_1.wav"
type="audio/wav">
Your browser does not support the <code>audio</code> element.
</audio>
......@@ -91,7 +91,7 @@ Audio samples generated from ground-truth spectrograms with a vocoder.
<td>
<audio controls="controls">
<source
src="https://paddlespeech.bj.bcebos.com/Parakeet/waveflow_res128_ljspeech_samples_1.0/step_2000k_sentence_2.wav"
src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/waveflow_res128_ljspeech_samples_1.0/step_2000k_sentence_2.wav"
type="audio/wav">
Your browser does not support the <code>audio</code> element.
</audio>
......@@ -110,7 +110,7 @@ Audio samples generated from ground-truth spectrograms with a vocoder.
<td>
<audio controls="controls">
<source
src="https://paddlespeech.bj.bcebos.com/Parakeet/waveflow_res128_ljspeech_samples_1.0/step_2000k_sentence_3.wav"
src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/waveflow_res128_ljspeech_samples_1.0/step_2000k_sentence_3.wav"
type="audio/wav">
Your browser does not support the <code>audio</code> element.
</audio>
......@@ -129,7 +129,7 @@ Audio samples generated from ground-truth spectrograms with a vocoder.
<td>
<audio controls="controls">
<source
src="https://paddlespeech.bj.bcebos.com/Parakeet/waveflow_res128_ljspeech_samples_1.0/step_2000k_sentence_4.wav"
src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/waveflow_res128_ljspeech_samples_1.0/step_2000k_sentence_4.wav"
type="audio/wav">
Your browser does not support the <code>audio</code> element.
</audio>
......@@ -281,7 +281,7 @@ Audio samples generated by a TTS system. Text is first transformed into spectrog
<td>
<audio controls="controls">
<source
src="https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_ljspeech_waveflow_samples_0.2/sentence_1.wav"
src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/tacotron2_ljspeech_waveflow_samples_0.2/sentence_1.wav"
type="audio/wav">
Your browser does not support the <code>audio</code> element.
</audio>
......@@ -300,7 +300,7 @@ Audio samples generated by a TTS system. Text is first transformed into spectrog
<td>
<audio controls="controls">
<source
src="https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_ljspeech_waveflow_samples_0.2/sentence_2.wav"
src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/tacotron2_ljspeech_waveflow_samples_0.2/sentence_2.wav"
type="audio/wav">
Your browser does not support the <code>audio</code> element.
</audio>
......@@ -320,7 +320,7 @@ Audio samples generated by a TTS system. Text is first transformed into spectrog
<td>
<audio controls="controls">
<source
src="https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_ljspeech_waveflow_samples_0.2/sentence_3.wav"
src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/tacotron2_ljspeech_waveflow_samples_0.2/sentence_3.wav"
type="audio/wav">
Your browser does not support the <code>audio</code> element.
</audio>
......@@ -341,7 +341,7 @@ Audio samples generated by a TTS system. Text is first transformed into spectrog
<td>
<audio controls="controls">
<source
src="https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_ljspeech_waveflow_samples_0.2/sentence_4.wav"
src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/tacotron2_ljspeech_waveflow_samples_0.2/sentence_4.wav"
type="audio/wav">
Your browser does not support the <code>audio</code> element.
</audio>
......@@ -361,7 +361,7 @@ Audio samples generated by a TTS system. Text is first transformed into spectrog
<td>
<audio controls="controls">
<source
src="https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_ljspeech_waveflow_samples_0.2/sentence_5.wav"
src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/tacotron2_ljspeech_waveflow_samples_0.2/sentence_5.wav"
type="audio/wav">
Your browser does not support the <code>audio</code> element.
</audio>
......@@ -381,7 +381,7 @@ Audio samples generated by a TTS system. Text is first transformed into spectrog
<td>
<audio controls="controls">
<source
src="https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_ljspeech_waveflow_samples_0.2/sentence_6.wav"
src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/tacotron2_ljspeech_waveflow_samples_0.2/sentence_6.wav"
type="audio/wav">
Your browser does not support the <code>audio</code> element.
</audio>
......@@ -401,7 +401,7 @@ Audio samples generated by a TTS system. Text is first transformed into spectrog
<td>
<audio controls="controls">
<source
src="https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_ljspeech_waveflow_samples_0.2/sentence_7.wav"
src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/tacotron2_ljspeech_waveflow_samples_0.2/sentence_7.wav"
type="audio/wav">
Your browser does not support the <code>audio</code> element.
</audio>
......@@ -421,7 +421,7 @@ Audio samples generated by a TTS system. Text is first transformed into spectrog
<td>
<audio controls="controls">
<source
src="https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_ljspeech_waveflow_samples_0.2/sentence_8.wav"
src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/tacotron2_ljspeech_waveflow_samples_0.2/sentence_8.wav"
type="audio/wav">
Your browser does not support the <code>audio</code> element.
</audio>
......@@ -441,7 +441,7 @@ Audio samples generated by a TTS system. Text is first transformed into spectrog
<td>
<audio controls="controls">
<source
src="https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_ljspeech_waveflow_samples_0.2/sentence_9.wav"
src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/tacotron2_ljspeech_waveflow_samples_0.2/sentence_9.wav"
type="audio/wav">
Your browser does not support the <code>audio</code> element.
</audio>
......
examples/aishell/README.md
浏览文件 @ d4ee5916
# ASR
* s0 for deepspeech2
* s1 for u2/transformer/conformer
* asr0 - deepspeech2 Streaming/Non-Streaming
* asr1 - transformer/conformer Streaming/Non-Streaming
* asr2 - transformer/conformer Streaming/Non-Streaming with Kaldi feature
## Data
......
examples/aishell/s0/local/data.sh → examples/aishell/asr0/local/data.sh
浏览文件 @ d4ee5916
......@@ -32,8 +32,8 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
--manifest_path="data/manifest.train.raw" \
--spectrum_type="linear" \
--delta_delta=false \
--stride_ms=10.0 \
--window_ms=20.0 \
--stride_ms=10 \
--window_ms=20 \
--sample_rate=16000 \
--use_dB_normalization=True \
--num_samples=2000 \
......@@ -66,7 +66,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
for dataset in train dev test; do
{
python3 ${MAIN_ROOT}/utils/format_data.py \
--feat_type "raw" \
--cmvn_path "data/mean_std.json" \
--unit_type "char" \
--vocab_path="data/vocab.txt" \
......
examples/aishell/s1/README.md → examples/aishell/asr1/README.md
浏览文件 @ d4ee5916
......@@ -19,3 +19,13 @@ Need set `decoding.decoding_chunk_size=16` when decoding.
| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_greedy_search | 16, -1 | - | 0.070806 |
| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | 16, -1 | - | 0.070739 |
| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | attention_rescoring | 16, -1 | - | 0.059400 |
## Transformer
| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |
| --- | --- | --- | --- | --- | --- | --- | --- |
| transformer | 31.95M | conf/transformer.yaml | spec_aug | test | attention | 3.858648955821991 | 0.057293 |
| transformer | 31.95M | conf/transformer.yaml | spec_aug | test | ctc_greedy_search | 3.858648955821991 | 0.061837 |
| transformer | 31.95M | conf/transformer.yaml | spec_aug | test | ctc_prefix_beam_search | 3.858648955821991 | 0.061685 |
| transformer | 31.95M | conf/transformer.yaml | spec_aug | test | attention_rescoring | 3.858648955821991 | 0.053844 |
\ No newline at end of file
examples/aishell/s1/conf/chunk_conformer.yaml → examples/aishell/asr1/conf/chunk_conformer.yaml
浏览文件 @ d4ee5916
......@@ -15,7 +15,7 @@ collator:
vocab_filepath: data/vocab.txt
unit_type: 'char'
spm_model_prefix: ''
augmentation_config: conf/augmentation.json
augmentation_config: conf/preprocess.yaml
batch_size: 32
raw_wav: True # use raw_wav or kaldi feature
spectrum_type: fbank #linear, mfcc, fbank
......@@ -38,7 +38,7 @@ collator:
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: conformer
......
examples/aishell/s1/conf/conformer.yaml → examples/aishell/asr1/conf/conformer.yaml
浏览文件 @ d4ee5916
......@@ -15,7 +15,7 @@ collator:
vocab_filepath: data/vocab.txt
unit_type: 'char'
spm_model_prefix: ''
augmentation_config: conf/augmentation.json
augmentation_config: conf/preprocess.yaml
batch_size: 64
raw_wav: True # use raw_wav or kaldi feature
spectrum_type: fbank #linear, mfcc, fbank
......@@ -37,7 +37,7 @@ collator:
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: conformer
......
examples/aishell/asr1/conf/preprocess.yaml 0 → 100644
浏览文件 @ d4ee5916
process:
# extract kaldi fbank from PCM
- type: fbank_kaldi
fs: 16000
n_mels: 80
n_shift: 160
win_length: 400
dither: true
- type: cmvn_json
cmvn_path: data/mean_std.json
# these three processes are a.k.a. SpecAugument
- type: time_warp
max_time_warp: 5
inplace: true
mode: PIL
- type: freq_mask
F: 30
n_mask: 2
inplace: true
replace_with_zero: false
- type: time_mask
T: 40
n_mask: 2
inplace: true
replace_with_zero: false
examples/aishell/asr1/conf/transformer.yaml 0 → 100644
浏览文件 @ d4ee5916
# 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: 0.5
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/preprocess.yaml
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: 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:
cmvn_file_type: "json"
# encoder related
encoder: transformer
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
# 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: null
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
training:
n_epoch: 120
accum_grad: 2
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.002
weight_decay: 1e-6
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: 128
error_rate_type: cer
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. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: False # simulate streaming inference. Defaults to False.
examples/aishell/s1/local/data.sh → examples/aishell/asr1/local/data.sh
浏览文件 @ d4ee5916
......@@ -33,8 +33,8 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
--spectrum_type="fbank" \
--feat_dim=80 \
--delta_delta=false \
--stride_ms=10.0 \
--window_ms=25.0 \
--stride_ms=10 \
--window_ms=25 \
--sample_rate=16000 \
--use_dB_normalization=False \
--num_samples=-1 \
......@@ -67,7 +67,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
for dataset in train dev test; do
{
python3 ${MAIN_ROOT}/utils/format_data.py \
--feat_type "raw" \
--cmvn_path "data/mean_std.json" \
--unit_type "char" \
--vocab_path="data/vocab.txt" \
......
examples/aishell/s1/local/test_hub.sh → examples/aishell/asr1/local/test_hub.sh
浏览文件 @ d4ee5916
......@@ -23,8 +23,6 @@ fi
# exit 1
#fi
for type in attention_rescoring; do
echo "decoding ${type}"
batch_size=1
......
examples/aishell3/tts3/README.md
浏览文件 @ d4ee5916
......@@ -97,7 +97,7 @@ optional arguments:
### Synthesize
We use [parallel wavegan](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc1) as the neural vocoder.
Download pretrained parallel wavegan model from [pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_aishell3_ckpt_0.5.zip) and unzip it.
Download pretrained parallel wavegan model from [pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip) and unzip it.
```bash
unzip pwg_aishell3_ckpt_0.5.zip
```
......@@ -202,7 +202,7 @@ optional arguments:
6. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
## Pretrained Model
Pretrained FastSpeech2 model with no silence in the edge of audios. [fastspeech2_nosil_aishell3_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_aishell3_ckpt_0.4.zip)
Pretrained FastSpeech2 model with no silence in the edge of audios. [fastspeech2_nosil_aishell3_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_ckpt_0.4.zip)
FastSpeech2 checkpoint contains files listed below.
......
examples/aishell3/vc0/README.md
浏览文件 @ d4ee5916
......@@ -41,7 +41,7 @@ We use Montreal Force Aligner 1.0. The label in aishell3 include pinyin，so th
We use [lexicon.txt](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/paddlespeech/t2s/exps/voice_cloning/tacotron2_ge2e/lexicon.txt) as the lexicon.
You can download the alignment results from here [alignment_aishell3.tar.gz](https://paddlespeech.bj.bcebos.com/Parakeet/alignment_aishell3.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/use_mfa) (use MFA1.x now) of our repo.
You can download the alignment results from here [alignment_aishell3.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/alignment_aishell3.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/use_mfa) (use MFA1.x now) of our repo.
```bash
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
......@@ -86,4 +86,4 @@ In addition, in order to accelerate the convergence of the model, we add `guided
CUDA_VISIBLE_DEVICES=${gpus} ./local/voice_cloning.sh ${ge2e_params_path} ${tacotron2_params_path} ${waveflow_params_path} ${vc_input} ${vc_output}
```
## Pretrained Model
[tacotron2_aishell3_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/tacotron2_aishell3_ckpt_0.3.zip).
[tacotron2_aishell3_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_aishell3_ckpt_0.3.zip).
examples/aishell3/vc1/README.md
浏览文件 @ d4ee5916
......@@ -22,7 +22,7 @@ You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech
## Pretrained GE2E model
We use pretrained GE2E model to generate spwaker embedding for each sentence.
Download pretrained GE2E model from here [ge2e_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/ge2e_ckpt_0.3.zip), and `unzip` it.
Download pretrained GE2E model from here [ge2e_ckpt_0.3.zip](https://bj.bcebos.com/paddlespeech/Parakeet/released_models/ge2e/ge2e_ckpt_0.3.zip), and `unzip` it.
## Get Started
Assume the path to the dataset is `~/datasets/data_aishell3`.
......@@ -84,7 +84,7 @@ The training step is very similar to that one of [tts3](https://github.com/Paddl
### Synthesize
We use [parallel wavegan](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc1) as the neural vocoder.
Download pretrained parallel wavegan model from [pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_aishell3_ckpt_0.5.zip) and unzip it.
Download pretrained parallel wavegan model from [pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip) and unzip it.
```bash
unzip pwg_aishell3_ckpt_0.5.zip
```
......@@ -115,7 +115,7 @@ ref_audio
CUDA_VISIBLE_DEVICES=${gpus} ./local/voice_cloning.sh ${conf_path} ${train_output_path} ${ckpt_name} ${ge2e_params_path} ${ref_audio_dir}
```
## Pretrained Model
[fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip)
[fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip)
FastSpeech2 checkpoint contains files listed below.
(There is no need for `speaker_id_map.txt` here )
......
examples/aishell3/voc1/README.md
浏览文件 @ d4ee5916
......@@ -132,7 +132,7 @@ optional arguments:
5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
## Pretrained Models
Pretrained models can be downloaded here [pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_aishell3_ckpt_0.5.zip).
Pretrained models can be downloaded here [pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip).
Parallel WaveGAN checkpoint contains files listed below.
......
examples/callcenter/s1/conf/chunk_conformer.yaml → examples/callcenter/asr1/conf/chunk_conformer.yaml
浏览文件 @ d4ee5916
......@@ -15,7 +15,7 @@ collator:
vocab_filepath: data/vocab.txt
unit_type: 'char'
spm_model_prefix: ''
augmentation_config: conf/augmentation.json
augmentation_config: conf/preprocess.yaml
batch_size: 32
raw_wav: True # use raw_wav or kaldi feature
spectrum_type: fbank #linear, mfcc, fbank
......@@ -38,7 +38,7 @@ collator:
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: conformer
......
examples/callcenter/s1/conf/conformer.yaml → examples/callcenter/asr1/conf/conformer.yaml
浏览文件 @ d4ee5916
......@@ -15,7 +15,7 @@ collator:
vocab_filepath: data/vocab.txt
unit_type: 'char'
spm_model_prefix: ''
augmentation_config: conf/augmentation.json
augmentation_config: conf/preprocess.yaml
batch_size: 32
raw_wav: True # use raw_wav or kaldi feature
spectrum_type: fbank #linear, mfcc, fbank
......@@ -37,7 +37,7 @@ collator:
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: conformer
......
examples/callcenter/asr1/conf/preprocess.yaml 0 → 100644
浏览文件 @ d4ee5916
process:
# extract kaldi fbank from PCM
- type: fbank_kaldi
fs: 16000
n_mels: 80
n_shift: 160
win_length: 400
dither: true
- type: cmvn_json
cmvn_path: data/mean_std.json
# these three processes are a.k.a. SpecAugument
- type: time_warp
max_time_warp: 5
inplace: true
mode: PIL
- type: freq_mask
F: 30
n_mask: 2
inplace: true
replace_with_zero: false
- type: time_mask
T: 40
n_mask: 2
inplace: true
replace_with_zero: false
examples/callcenter/s1/local/data.sh → examples/callcenter/asr1/local/data.sh
浏览文件 @ d4ee5916
......@@ -21,8 +21,8 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
--spectrum_type="fbank" \
--feat_dim=80 \
--delta_delta=false \
--stride_ms=10.0 \
--window_ms=25.0 \
--stride_ms=10 \
--window_ms=25 \
--sample_rate=8000 \
--use_dB_normalization=False \
--num_samples=-1 \
......@@ -55,7 +55,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
for dataset in train dev test; do
{
python3 ${MAIN_ROOT}/utils/format_data.py \
--feat_type "raw" \
--cmvn_path "data/mean_std.json" \
--unit_type "char" \
--vocab_path="data/vocab.txt" \
......
examples/csmsc/tts2/README.md
浏览文件 @ d4ee5916
......@@ -90,7 +90,7 @@ optional arguments:
### Synthesize
We use [parallel wavegan](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc1) as the neural vocoder.
Download pretrained parallel wavegan model from [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip) and unzip it.
Download pretrained parallel wavegan model from [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip) and unzip it.
```bash
unzip pwg_baker_ckpt_0.4.zip
```
......@@ -208,9 +208,9 @@ CUDA_VISIBLE_DEVICES=${gpus} ./local/inference.sh ${train_output_path}
```
## Pretrained Model
Pretrained SpeedySpeech model with no silence in the edge of audios[speedyspeech_nosil_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/speedyspeech_nosil_baker_ckpt_0.5.zip).
Pretrained SpeedySpeech model with no silence in the edge of audios[speedyspeech_nosil_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_ckpt_0.5.zip).
Static model can be downloaded here [speedyspeech_nosil_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/speedyspeech_nosil_baker_static_0.5.zip).
Static model can be downloaded here [speedyspeech_nosil_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_nosil_baker_static_0.5.zip).
SpeedySpeech checkpoint contains files listed below.
```text
......
examples/csmsc/tts3/README.md
浏览文件 @ d4ee5916
......@@ -88,7 +88,7 @@ optional arguments:
### Synthesize
We use [parallel wavegan](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc1) as the neural vocoder.
Download pretrained parallel wavegan model from [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip) and unzip it.
Download pretrained parallel wavegan model from [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip) and unzip it.
```bash
unzip pwg_baker_ckpt_0.4.zip
```
......@@ -199,9 +199,9 @@ CUDA_VISIBLE_DEVICES=${gpus} ./local/inference.sh ${train_output_path}
```
## Pretrained Model
Pretrained FastSpeech2 model with no silence in the edge of audios [fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_baker_ckpt_0.4.zip).
Pretrained FastSpeech2 model with no silence in the edge of audios [fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip).
Static model can be downloaded here [fastspeech2_nosil_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_baker_static_0.4.zip).
Static model can be downloaded here [fastspeech2_nosil_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_static_0.4.zip).
FastSpeech2 checkpoint contains files listed below.
```text
......
examples/csmsc/voc1/README.md
浏览文件 @ d4ee5916
......@@ -122,9 +122,9 @@ optional arguments:
5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
## Pretrained Models
Pretrained model can be downloaded here [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip).
Pretrained model can be downloaded here [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip).
Static model can be downloaded here [pwg_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_static_0.4.zip).
Static model can be downloaded here [pwg_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_static_0.4.zip).
Parallel WaveGAN checkpoint contains files listed below.
......
examples/csmsc/voc3/README.md
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......@@ -113,7 +113,7 @@ The length of mel-spectrograms should align with the length of wavs, so we shoul
But since we are fine-tuning, we should use the statistics computed during training step.
You should first download pretrained `FastSpeech2` model from [fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_baker_ckpt_0.4.zip) and `unzip` it.
You should first download pretrained `FastSpeech2` model from [fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip) and `unzip` it.
Assume the path to the dump-dir of training step is `dump`.
Assume the path to the duration result of CSMSC is `durations.txt` (generated during training step's preprocessing).
......@@ -147,11 +147,11 @@ TODO:
The hyperparameter of `finetune.yaml` is not good enough, a smaller `learning_rate` should be used (more `milestones` should be set).
## Pretrained Models
Pretrained model can be downloaded here [mb_melgan_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/mb_melgan_baker_ckpt_0.5.zip).
Pretrained model can be downloaded here [mb_melgan_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_ckpt_0.5.zip).
Finetuned model can ben downloaded here [mb_melgan_baker_finetune_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/mb_melgan_baker_finetune_ckpt_0.5.zip).
Finetuned model can ben downloaded here [mb_melgan_baker_finetune_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_finetune_ckpt_0.5.zip).
Static model can be downloaded here [mb_melgan_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/mb_melgan_baker_static_0.5.zip)
Static model can be downloaded here [mb_melgan_baker_static_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_static_0.5.zip)
Multi Band MelGAN checkpoint contains files listed below.
......
examples/dataset/aidatatang_200zh/aidatatang_200zh.py
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......@@ -22,6 +22,7 @@ import argparse
import codecs
import json
import os
from pathlib import Path
import soundfile
......@@ -79,6 +80,7 @@ def create_manifest(data_dir, manifest_path_prefix):
audio_path = os.path.abspath(os.path.join(subfolder, fname))
audio_id = os.path.basename(fname)[:-4]
utt2spk = Path(audio_path).parent.name
audio_data, samplerate = soundfile.read(audio_path)
duration = float(len(audio_data) / samplerate)
......@@ -87,6 +89,7 @@ def create_manifest(data_dir, manifest_path_prefix):
json.dumps(
{
'utt': audio_id,
'utt2spk': str(utt2spk),
'feat': audio_path,
'feat_shape': (duration, ), # second
'text': text,
......
examples/dataset/aishell/aishell.py
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......@@ -22,6 +22,7 @@ import argparse
import codecs
import json
import os
from pathlib import Path
import soundfile
......@@ -81,6 +82,8 @@ def create_manifest(data_dir, manifest_path_prefix):
# if no transcription for audio then skipped
if audio_id not in transcript_dict:
continue
utt2spk = Path(audio_path).parent.name
audio_data, samplerate = soundfile.read(audio_path)
duration = float(len(audio_data) / samplerate)
text = transcript_dict[audio_id]
......@@ -88,6 +91,7 @@ def create_manifest(data_dir, manifest_path_prefix):
json.dumps(
{
'utt': audio_id,
'utt2spk': str(utt2spk),
'feat': audio_path,
'feat_shape': (duration, ), # second
'text': text
......
examples/dataset/librispeech/librispeech.py
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......@@ -78,7 +78,7 @@ def create_manifest(data_dir, manifest_path):
print("Creating manifest %s ..." % manifest_path)
json_lines = []
total_sec = 0.0
total_text = 0.0
total_char = 0.0
total_num = 0
for subfolder, _, filelist in sorted(os.walk(data_dir)):
......@@ -89,25 +89,28 @@ def create_manifest(data_dir, manifest_path):
text_filepath = os.path.join(subfolder, text_filelist[0])
for line in io.open(text_filepath, encoding="utf8"):
segments = line.strip().split()
nchars = len(segments[1:])
text = ' '.join(segments[1:]).lower()
audio_filepath = os.path.abspath(
os.path.join(subfolder, segments[0] + '.flac'))
audio_data, samplerate = soundfile.read(audio_filepath)
duration = float(len(audio_data)) / samplerate
utt = os.path.splitext(os.path.basename(audio_filepath))[0]
utt2spk = '-'.join(utt.split('-')[:2])
json_lines.append(
json.dumps({
'utt':
os.path.splitext(os.path.basename(audio_filepath))[0],
'feat':
audio_filepath,
'feat_shape': (duration, ), #second
'text':
text
'utt': utt,
'utt2spk': utt2spk,
'feat': audio_filepath,
'feat_shape': (duration, ), # second
'text': text,
}))
total_sec += duration
total_text += len(text)
total_char += nchars
total_num += 1
with codecs.open(manifest_path, 'w', 'utf-8') as out_file:
......@@ -122,8 +125,8 @@ def create_manifest(data_dir, manifest_path):
print(f"{subset}:", file=f)
print(f"{total_num} utts", file=f)
print(f"{total_sec / (60*60)} h", file=f)
print(f"{total_text} text", file=f)
print(f"{total_text / total_sec} text/sec", file=f)
print(f"{total_char} char", file=f)
print(f"{total_char / total_sec} char/sec", file=f)
print(f"{total_sec / total_num} sec/utt", file=f)
......
examples/dataset/mini_librispeech/mini_librispeech.py
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......@@ -74,15 +74,16 @@ def create_manifest(data_dir, manifest_path):
audio_filepath = os.path.join(subfolder, segments[0] + '.flac')
audio_data, samplerate = soundfile.read(audio_filepath)
duration = float(len(audio_data)) / samplerate
utt = os.path.splitext(os.path.basename(audio_filepath))[0]
utt2spk = '-'.join(utt.split('-')[:2])
json_lines.append(
json.dumps({
'utt':
os.path.splitext(os.path.basename(audio_filepath))[0],
'feat':
audio_filepath,
'utt': utt,
'utt2spk': utt2spk,
'feat': audio_filepath,
'feat_shape': (duration, ), #second
'text':
text
'text': text,
}))
total_sec += duration
......
examples/dataset/ted_en_zh/ted_en_zh.py
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......@@ -72,14 +72,16 @@ def create_manifest(data_dir, manifest_path_prefix):
continue
audio_data, samplerate = soundfile.read(audio_path)
duration = float(len(audio_data) / samplerate)
translation_str = " ".join(translation.split())
trancription_str = " ".join(trancription.split())
json_lines.append(
json.dumps(
{
'utt': utt,
'feat': audio_path,
'feat_shape': (duration, ), # second
'text': " ".join(translation.split()),
'text1': " ".join(trancription.split())
'text': [translation_str, trancription_str],
},
ensure_ascii=False))
......
examples/dataset/thchs30/thchs30.py
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......@@ -113,6 +113,8 @@ def create_manifest(data_dir, manifest_path_prefix):
assert os.path.exists(audio_path) and os.path.exists(text_path)
audio_id = os.path.basename(audio_path)[:-4]
spk = audio_id.split('_')[0]
word_text, syllable_text, phone_text = read_trn(text_path)
audio_data, samplerate = soundfile.read(audio_path)
duration = float(len(audio_data) / samplerate)
......@@ -122,6 +124,7 @@ def create_manifest(data_dir, manifest_path_prefix):
json.dumps(
{
'utt': audio_id,
'utt2spk': spk,
'feat': audio_path,
'feat_shape': (duration, ), # second
'text': word_text, # charactor
......
examples/dataset/timit/timit.py
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......@@ -180,12 +180,12 @@ def create_manifest(data_dir, manifest_path_prefix):
json.dumps(
{
'utt': utt_id,
'utt2spk': spk,
'utt2gender': gender,
'feat': str(audio_path),
'feat_shape': (duration, ), # second
'text': word_text, # word
'phone': phone_text,
'spk': spk,
'gender': gender,
},
ensure_ascii=False))
......
examples/dataset/timit/timit_kaldi_standard_split.py
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......@@ -22,6 +22,7 @@ import argparse
import codecs
import json
import os
from pathlib import Path
import soundfile
......@@ -67,10 +68,17 @@ def create_manifest(data_dir, manifest_path_prefix):
audio_data, samplerate = soundfile.read(audio_path)
duration = float(len(audio_data) / samplerate)
text = phn_dict[audio_id]
gender_spk = str(Path(audio_path).parent.stem)
spk = gender_spk[1:]
gender = gender_spk[0]
utt_id = '_'.join([spk, gender, audio_id])
json_lines.append(
json.dumps(
{
'utt': audio_id,
'utt2spk': spk,
'utt2gender': gender,
'feat': audio_path,
'feat_shape': (duration, ), # second
'text': text
......
examples/dataset/voxforge/voxforge.py
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......@@ -175,9 +175,12 @@ def generate_manifest(data_dir, manifest_path):
audio_data, samplerate = soundfile.read(u)
duration = float(len(audio_data)) / samplerate
utt = os.path.splitext(os.path.basename(u))[0]
json_lines.append(
json.dumps({
'utt': os.path.splitext(os.path.basename(u))[0],
'utt': utt,
'utt2spk': speaker,
'feat': u,
'feat_shape': (duration, ), #second
'text': trans.lower()
......
examples/librispeech/README.md
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# ASR
* s0 is for deepspeech2 offline
* s1 is for transformer/conformer/U2
* s2 is for transformer/conformer/U2 w/ kaldi feat, need install Kaldi
* asr0 - deepspeech2 Streaming/Non-Streaming
* asr1 - transformer/conformer Streaming/Non-Streaming
* asr2 - transformer/conformer Streaming/Non-Streaming with Kaldi feature
## Data
| Data Subset | Duration in Seconds |
......
examples/librispeech/s0/local/data.sh → examples/librispeech/asr0/local/data.sh
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......@@ -50,8 +50,8 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
--spectrum_type="linear" \
--delta_delta=false \
--sample_rate=16000 \
--stride_ms=10.0 \
--window_ms=20.0 \
--stride_ms=10 \
--window_ms=20 \
--use_dB_normalization=True \
--num_workers=${num_workers} \
--output_path="data/mean_std.json"
......@@ -81,7 +81,6 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
for set in train dev test dev-clean dev-other test-clean test-other; do
{
python3 ${MAIN_ROOT}/utils/format_data.py \
--feat_type "raw" \
--cmvn_path "data/mean_std.json" \
--unit_type ${unit_type} \
--vocab_path="data/vocab.txt" \
......
examples/librispeech/s1/README.md → examples/librispeech/asr1/README.md
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......@@ -21,7 +21,7 @@
## Transformer
| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |
| --- | --- | --- | --- | --- | --- | --- | --- |
| transformer | 32.52 M | conf/transformer.yaml | spec_aug + shift | test-clean | attention | 7.404532432556152 | 0.056204 |
| transformer | 32.52 M | conf/transformer.yaml | spec_aug + shift | test-clean | ctc_greedy_search | 7.404532432556152 | 0.058658 |
| transformer | 32.52 M | conf/transformer.yaml | spec_aug + shift | test-clean | ctc_prefix_beam_search | 7.404532432556152 | 0.058278 |
| transformer | 32.52 M | conf/transformer.yaml | spec_aug + shift | test-clean | attention_rescoring | 7.404532432556152 | 0.045591 |
| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | attention | 6.805267604192098, | 0.049795 |
| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | ctc_greedy_search | 6.805267604192098, | 0.054892 |
| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | ctc_prefix_beam_search | 6.805267604192098, | 0.054531 |
| transformer | 32.52 M | conf/transformer.yaml | spec_aug | test-clean | attention_rescoring | 6.805267604192098, | 0.042244 |
examples/librispeech/s1/conf/chunk_conformer.yaml → examples/librispeech/asr1/conf/chunk_conformer.yaml
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......@@ -15,7 +15,7 @@ collator:
unit_type: 'spm'
spm_model_prefix: 'data/bpe_unigram_5000'
mean_std_filepath: ""
augmentation_config: conf/augmentation.json
augmentation_config: conf/preprocess.yaml
batch_size: 16
raw_wav: True # use raw_wav or kaldi feature
spectrum_type: fbank #linear, mfcc, fbank
......@@ -38,7 +38,7 @@ collator:
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: conformer
......
examples/librispeech/s1/conf/chunk_transformer.yaml → examples/librispeech/asr1/conf/chunk_transformer.yaml
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......@@ -15,7 +15,7 @@ collator:
unit_type: 'spm'
spm_model_prefix: 'data/bpe_unigram_5000'
mean_std_filepath: ""
augmentation_config: conf/augmentation.json
augmentation_config: conf/preprocess.yaml
batch_size: 64
raw_wav: True # use raw_wav or kaldi feature
spectrum_type: fbank #linear, mfcc, fbank
......@@ -38,7 +38,7 @@ collator:
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: transformer
......
examples/librispeech/s1/conf/conformer.yaml → examples/librispeech/asr1/conf/conformer.yaml
浏览文件 @ d4ee5916
......@@ -15,7 +15,7 @@ collator:
unit_type: 'spm'
spm_model_prefix: 'data/bpe_unigram_5000'
mean_std_filepath: ""
augmentation_config: conf/augmentation.json
augmentation_config: conf/preprocess.yaml
batch_size: 16
raw_wav: True # use raw_wav or kaldi feature
spectrum_type: fbank #linear, mfcc, fbank
......@@ -38,7 +38,7 @@ collator:
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: conformer
......
examples/librispeech/asr1/conf/preprocess.yaml 0 → 100644
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process:
# extract kaldi fbank from PCM
- type: fbank_kaldi
fs: 16000
n_mels: 80
n_shift: 160
win_length: 400
dither: true
- type: cmvn_json
cmvn_path: data/mean_std.json
# these three processes are a.k.a. SpecAugument
- type: time_warp
max_time_warp: 5
inplace: true
mode: PIL
- type: freq_mask
F: 30
n_mask: 2
inplace: true
replace_with_zero: false
- type: time_mask
T: 40
n_mask: 2
inplace: true
replace_with_zero: false
examples/librispeech/s1/conf/transformer.yaml → examples/librispeech/asr1/conf/transformer.yaml
浏览文件 @ d4ee5916
......@@ -15,7 +15,7 @@ collator:
unit_type: 'spm'
spm_model_prefix: 'data/bpe_unigram_5000'
mean_std_filepath: ""
augmentation_config: conf/augmentation.json
augmentation_config: conf/preprocess.yaml
batch_size: 32
raw_wav: True # use raw_wav or kaldi feature
spectrum_type: fbank #linear, mfcc, fbank
......@@ -38,7 +38,7 @@ collator:
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file:
cmvn_file_type: "json"
# encoder related
encoder: transformer
......
examples/librispeech/s1/local/data.sh → examples/librispeech/asr1/local/data.sh
浏览文件 @ d4ee5916
......@@ -8,6 +8,11 @@ nbpe=5000
bpemode=unigram
bpeprefix="data/bpe_${bpemode}_${nbpe}"
stride_ms=10
window_ms=25
sample_rate=16000
feat_dim=80
source ${MAIN_ROOT}/utils/parse_options.sh
......@@ -27,21 +32,21 @@ if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
exit 1
fi
for set in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do
mv data/manifest.${set} data/manifest.${set}.raw
for sub in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do
mv data/manifest.${sub} data/manifest.${sub}.raw
done
rm -rf data/manifest.train.raw data/manifest.dev.raw data/manifest.test.raw
for set in train-clean-100 train-clean-360 train-other-500; do
cat data/manifest.${set}.raw >> data/manifest.train.raw
for sub in train-clean-100 train-clean-360 train-other-500; do
cat data/manifest.${sub}.raw >> data/manifest.train.raw
done
for set in dev-clean dev-other; do
cat data/manifest.${set}.raw >> data/manifest.dev.raw
for sub in dev-clean dev-other; do
cat data/manifest.${sub}.raw >> data/manifest.dev.raw
done
for set in test-clean test-other; do
cat data/manifest.${set}.raw >> data/manifest.test.raw
for sub in test-clean test-other; do
cat data/manifest.${sub}.raw >> data/manifest.test.raw
done
fi
......@@ -52,11 +57,11 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
--manifest_path="data/manifest.train.raw" \
--num_samples=-1 \
--spectrum_type="fbank" \
--feat_dim=80 \
--feat_dim=${feat_dim} \
--delta_delta=false \
--sample_rate=16000 \
--stride_ms=10.0 \
--window_ms=25.0 \
--sample_rate=${sample_rate} \
--stride_ms=${stride_ms} \
--window_ms=${window_ms} \
--use_dB_normalization=False \
--num_workers=${num_workers} \
--output_path="data/mean_std.json"
......@@ -85,16 +90,15 @@ fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# format manifest with tokenids, vocab size
for set in train dev test dev-clean dev-other test-clean test-other; do
for sub in train dev test dev-clean dev-other test-clean test-other; do
{
python3 ${MAIN_ROOT}/utils/format_data.py \
--feat_type "raw" \
--cmvn_path "data/mean_std.json" \
--unit_type "spm" \
--spm_model_prefix ${bpeprefix} \
--vocab_path="data/vocab.txt" \
--manifest_path="data/manifest.${set}.raw" \
--output_path="data/manifest.${set}"
--manifest_path="data/manifest.${sub}.raw" \
--output_path="data/manifest.${sub}"
if [ $? -ne 0 ]; then
echo "Formt mnaifest failed. Terminated."
......@@ -103,6 +107,16 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
}&
done
wait
for sub in train dev; do
mv data/manifest.${sub} data/manifest.${sub}.fmt
done
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
for sub in train dev; do
remove_longshortdata.py --maxframes 3000 --maxchars 400 --stride_ms ${stride_ms} data/manifest.${sub}.fmt data/manifest.${sub}
done
fi
echo "LibriSpeech Data preparation done."
......
examples/ljspeech/tts0/README.md
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