# espnet
**Repository Path**: monkeycc/espnet
## Basic Information
- **Project Name**: espnet
- **Description**: https://github.com/espnet/espnet
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-02-28
- **Last Updated**: 2022-05-24
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
expand
We list the character error rate (CER) and word error rate (WER) of major ASR tasks.
| Task | CER (%) | WER (%) | Pretrained model|
| ----------- | :----: | :----: | :----: |
| Aishell dev/test | 4.6/5.1 | N/A | [link](https://github.com/espnet/espnet/blob/master/egs/aishell/asr1/RESULTS.md#conformer-kernel-size--15--specaugment--lm-weight--00-result) |
| **ESPnet2** Aishell dev/test | 4.4/4.7 | N/A | [link](https://github.com/espnet/espnet/tree/master/egs2/aishell/asr1#conformer--specaug--speed-perturbation-featsraw-n_fft512-hop_length128) |
| Common Voice dev/test | 1.7/1.8 | 2.2/2.3 | [link](https://github.com/espnet/espnet/blob/master/egs/commonvoice/asr1/RESULTS.md#first-results-default-pytorch-transformer-setting-with-bpe-100-epochs-single-gpu) |
| CSJ eval1/eval2/eval3 | 5.7/3.8/4.2 | N/A | [link](https://github.com/espnet/espnet/blob/master/egs/csj/asr1/RESULTS.md#pytorch-backend-transformer-without-any-hyperparameter-tuning) |
| **ESPnet2** CSJ eval1/eval2/eval3 | 4.5/3.3/3.6 | N/A | [link](https://github.com/espnet/espnet/tree/master/egs2/csj/asr1#initial-conformer-results) |
| HKUST dev | 23.5 | N/A | [link](https://github.com/espnet/espnet/blob/master/egs/hkust/asr1/RESULTS.md#transformer-only-20-epochs) |
| **ESPnet2** HKUST dev | 21.2 | N/A | [link](https://github.com/espnet/espnet/tree/master/egs2/hkust/asr1#transformer-asr--transformer-lm) |
| Librispeech dev_clean/dev_other/test_clean/test_other | N/A | 1.9/4.9/2.1/4.9 | [link](https://github.com/espnet/espnet/blob/master/egs/librispeech/asr1/RESULTS.md#pytorch-large-conformer-with-specaug--speed-perturbation-8-gpus--transformer-lm-4-gpus) |
| **ESPnet2** Librispeech dev_clean/dev_other/test_clean/test_other | 0.6/1.5/0.6/1.4 | 1.7/3.4/1.8/3.6 | [link](https://github.com/espnet/espnet/tree/master/egs2/librispeech/asr1#self-supervised-learning-features-hubert_large_ll60k-conformer-utt_mvn-with-transformer-lm) |
| Switchboard (eval2000) callhm/swbd | N/A | 14.0/6.8 | [link](https://github.com/espnet/espnet/blob/master/egs/swbd/asr1/RESULTS.md#conformer-with-bpe-2000-specaug-speed-perturbation-transformer-lm-decoding) |
| TEDLIUM2 dev/test | N/A | 8.6/7.2 | [link](https://github.com/espnet/espnet/blob/master/egs/tedlium2/asr1/RESULTS.md#conformer-large-model--specaug--speed-perturbation--rnnlm) |
| TEDLIUM3 dev/test | N/A | 9.6/7.6 | [link](https://github.com/espnet/espnet/blob/master/egs/tedlium3/asr1/RESULTS.md) |
| WSJ dev93/eval92 | 3.2/2.1 | 7.0/4.7 | N/A |
| **ESPnet2** WSJ dev93/eval92 | 1.1/0.8 | 2.8/1.8 | [link](https://github.com/espnet/espnet/tree/master/egs2/wsj/asr1#self-supervised-learning-features-wav2vec2_large_ll60k-conformer-utt_mvn-with-transformer-lm) |
Note that the performance of the CSJ, HKUST, and Librispeech tasks was significantly improved by using the wide network (#units = 1024) and large subword units if necessary reported by [RWTH](https://arxiv.org/pdf/1805.03294.pdf).
If you want to check the results of the other recipes, please check `egs//asr1/RESULTS.md`.
expand
You can recognize speech in a WAV file using pretrained models.
Go to a recipe directory and run `utils/recog_wav.sh` as follows:
```sh
# go to recipe directory and source path of espnet tools
cd egs/tedlium2/asr1 && . ./path.sh
# let's recognize speech!
recog_wav.sh --models tedlium2.transformer.v1 example.wav
```
where `example.wav` is a WAV file to be recognized.
The sampling rate must be consistent with that of data used in training.
Available pretrained models in the demo script are listed as below.
| Model | Notes |
| :------ | :------ |
| [tedlium2.rnn.v1](https://drive.google.com/open?id=1UqIY6WJMZ4sxNxSugUqp3mrGb3j6h7xe) | Streaming decoding based on CTC-based VAD |
| [tedlium2.rnn.v2](https://drive.google.com/open?id=1cac5Uc09lJrCYfWkLQsF8eapQcxZnYdf) | Streaming decoding based on CTC-based VAD (batch decoding) |
| [tedlium2.transformer.v1](https://drive.google.com/open?id=1cVeSOYY1twOfL9Gns7Z3ZDnkrJqNwPow) | Joint-CTC attention Transformer trained on Tedlium 2 |
| [tedlium3.transformer.v1](https://drive.google.com/open?id=1zcPglHAKILwVgfACoMWWERiyIquzSYuU) | Joint-CTC attention Transformer trained on Tedlium 3 |
| [librispeech.transformer.v1](https://drive.google.com/open?id=1BtQvAnsFvVi-dp_qsaFP7n4A_5cwnlR6) | Joint-CTC attention Transformer trained on Librispeech |
| [commonvoice.transformer.v1](https://drive.google.com/open?id=1tWccl6aYU67kbtkm8jv5H6xayqg1rzjh) | Joint-CTC attention Transformer trained on CommonVoice |
| [csj.transformer.v1](https://drive.google.com/open?id=120nUQcSsKeY5dpyMWw_kI33ooMRGT2uF) | Joint-CTC attention Transformer trained on CSJ |
| [csj.rnn.v1](https://drive.google.com/open?id=1ALvD4nHan9VDJlYJwNurVr7H7OV0j2X9) | Joint-CTC attention VGGBLSTM trained on CSJ |
expand
We list results from three different models on WSJ0-2mix, which is one the most widely used benchmark dataset for speech separation.
|Model|STOI|SAR|SDR|SIR|
|---|---|---|---|---|
|[TF Masking](https://zenodo.org/record/4498554)|0.89|11.40|10.24|18.04|
|[Conv-Tasnet](https://zenodo.org/record/4498562)|0.95|16.62|15.94|25.90|
|[DPRNN-Tasnet](https://zenodo.org/record/4688000)|0.96|18.82|18.29|28.92|
expand
You can try the interactive demo with Google Colab. Please click the following button to get access to the demos.
[](https://colab.research.google.com/drive/1fjRJCh96SoYLZPRxsjF9VDv4Q2VoIckI?usp=sharing)
It is based on ESPnet2. Pretrained models are available for both speech enhancement and speech separation tasks.
expand
We list 4-gram BLEU of major ST tasks.
#### end-to-end system
| Task | BLEU | Pretrained model |
| ---- | :----: | :----: |
| Fisher-CallHome Spanish fisher_test (Es->En) | 51.03 | [link](https://github.com/espnet/espnet/blob/master/egs/fisher_callhome_spanish/st1/RESULTS.md#train_spen_lcrm_pytorch_train_pytorch_transformer_bpe_short_long_bpe1000_specaug_asrtrans_mttrans) |
| Fisher-CallHome Spanish callhome_evltest (Es->En) | 20.44 | [link](https://github.com/espnet/espnet/blob/master/egs/fisher_callhome_spanish/st1/RESULTS.md#train_spen_lcrm_pytorch_train_pytorch_transformer_bpe_short_long_bpe1000_specaug_asrtrans_mttrans) |
| Libri-trans test (En->Fr) | 16.70 | [link](https://github.com/espnet/espnet/blob/master/egs/libri_trans/st1/RESULTS.md#train_spfr_lc_pytorch_train_pytorch_transformer_bpe_short_long_bpe1000_specaug_asrtrans_mttrans-1) |
| How2 dev5 (En->Pt) | 45.68 | [link](https://github.com/espnet/espnet/blob/master/egs/how2/st1/RESULTS.md#trainpt_tc_pytorch_train_pytorch_transformer_short_long_bpe8000_specaug_asrtrans_mttrans-1) |
| Must-C tst-COMMON (En->De) | 22.91 | [link](https://github.com/espnet/espnet/blob/master/egs/must_c/st1/RESULTS.md#train_spen-dede_tc_pytorch_train_pytorch_transformer_short_long_bpe8000_specaug_asrtrans_mttrans) |
| Mboshi-French dev (Fr->Mboshi) | 6.18 | N/A |
#### cascaded system
| Task | BLEU | Pretrained model |
| ---- | :----: | :----: |
| Fisher-CallHome Spanish fisher_test (Es->En) | 42.16 | N/A |
| Fisher-CallHome Spanish callhome_evltest (Es->En) | 19.82 | N/A |
| Libri-trans test (En->Fr) | 16.96 | N/A |
| How2 dev5 (En->Pt) | 44.90 | N/A |
| Must-C tst-COMMON (En->De) | 23.65 | N/A |
If you want to check the results of the other recipes, please check `egs//st1/RESULTS.md`.
expand
(**New!**) We made a new real-time E2E-ST + TTS demonstration in Google Colab.
Please access the notebook from the following button and enjoy the real-time speech-to-speech translation!
[](https://colab.research.google.com/github/espnet/notebook/blob/master/st_demo.ipynb)
---
You can translate speech in a WAV file using pretrained models.
Go to a recipe directory and run `utils/translate_wav.sh` as follows:
```sh
# go to recipe directory and source path of espnet tools
cd egs/fisher_callhome_spanish/st1 && . ./path.sh
# download example wav file
wget -O - https://github.com/espnet/espnet/files/4100928/test.wav.tar.gz | tar zxvf -
# let's translate speech!
translate_wav.sh --models fisher_callhome_spanish.transformer.v1.es-en test.wav
```
where `test.wav` is a WAV file to be translated.
The sampling rate must be consistent with that of data used in training.
Available pretrained models in the demo script are listed as below.
| Model | Notes |
| :------ | :------ |
| [fisher_callhome_spanish.transformer.v1](https://drive.google.com/open?id=1hawp5ZLw4_SIHIT3edglxbKIIkPVe8n3) | Transformer-ST trained on Fisher-CallHome Spanish Es->En |
expand
| Task | BLEU | Pretrained model |
| ---- | :----: | :----: |
| Fisher-CallHome Spanish fisher_test (Es->En) | 61.45 | [link](https://github.com/espnet/espnet/blob/master/egs/fisher_callhome_spanish/mt1/RESULTS.md#trainen_lcrm_lcrm_pytorch_train_pytorch_transformer_bpe_bpe1000) |
| Fisher-CallHome Spanish callhome_evltest (Es->En) | 29.86 | [link](https://github.com/espnet/espnet/blob/master/egs/fisher_callhome_spanish/mt1/RESULTS.md#trainen_lcrm_lcrm_pytorch_train_pytorch_transformer_bpe_bpe1000) |
| Libri-trans test (En->Fr) | 18.09 | [link](https://github.com/espnet/espnet/blob/master/egs/libri_trans/mt1/RESULTS.md#trainfr_lcrm_tc_pytorch_train_pytorch_transformer_bpe1000) |
| How2 dev5 (En->Pt) | 58.61 | [link](https://github.com/espnet/espnet/blob/master/egs/how2/mt1/RESULTS.md#trainpt_tc_tc_pytorch_train_pytorch_transformer_bpe8000) |
| Must-C tst-COMMON (En->De) | 27.63 | [link](https://github.com/espnet/espnet/blob/master/egs/must_c/mt1/RESULTS.md#summary-4-gram-bleu) |
| IWSLT'14 test2014 (En->De) | 24.70 | [link](https://github.com/espnet/espnet/blob/master/egs/iwslt16/mt1/RESULTS.md#result) |
| IWSLT'14 test2014 (De->En) | 29.22 | [link](https://github.com/espnet/espnet/blob/master/egs/iwslt16/mt1/RESULTS.md#result) |
| IWSLT'16 test2014 (En->De) | 24.05 | [link](https://github.com/espnet/espnet/blob/master/egs/iwslt16/mt1/RESULTS.md#result) |
| IWSLT'16 test2014 (De->En) | 29.13 | [link](https://github.com/espnet/espnet/blob/master/egs/iwslt16/mt1/RESULTS.md#result) |
ESPnet2
You can listen to the generated samples in the following URL.
- [ESPnet2 TTS generated samples](https://drive.google.com/drive/folders/1H3fnlBbWMEkQUfrHqosKN_ZX_WjO29ma?usp=sharing)
> Note that in the generation we use Griffin-Lim (`wav/`) and [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN) (`wav_pwg/`).
You can download pretrained models via `espnet_model_zoo`.
- [ESPnet model zoo](https://github.com/espnet/espnet_model_zoo)
- [Pretrained model list](https://github.com/espnet/espnet_model_zoo/blob/master/espnet_model_zoo/table.csv)
You can download pretrained vocoders via `kan-bayashi/ParallelWaveGAN`.
- [kan-bayashi/ParallelWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN)
- [Pretrained vocoder list](https://github.com/kan-bayashi/ParallelWaveGAN#results)
ESPnet1
> NOTE: We are moving on ESPnet2-based development for TTS. Please check the latest results in the above ESPnet2 results.
You can listen to our samples in demo HP [espnet-tts-sample](https://espnet.github.io/espnet-tts-sample/).
Here we list some notable ones:
- [Single English speaker Tacotron2](https://drive.google.com/open?id=18JgsOCWiP_JkhONasTplnHS7yaF_konr)
- [Single Japanese speaker Tacotron2](https://drive.google.com/open?id=1fEgS4-K4dtgVxwI4Pr7uOA1h4PE-zN7f)
- [Single other language speaker Tacotron2](https://drive.google.com/open?id=1q_66kyxVZGU99g8Xb5a0Q8yZ1YVm2tN0)
- [Multi English speaker Tacotron2](https://drive.google.com/open?id=18S_B8Ogogij34rIfJOeNF8D--uG7amz2)
- [Single English speaker Transformer](https://drive.google.com/open?id=14EboYVsMVcAq__dFP1p6lyoZtdobIL1X)
- [Single English speaker FastSpeech](https://drive.google.com/open?id=1PSxs1VauIndwi8d5hJmZlppGRVu2zuy5)
- [Multi English speaker Transformer](https://drive.google.com/open?id=1_vrdqjM43DdN1Qz7HJkvMQ6lCMmWLeGp)
- [Single Italian speaker FastSpeech](https://drive.google.com/open?id=13I5V2w7deYFX4DlVk1-0JfaXmUR2rNOv)
- [Single Mandarin speaker Transformer](https://drive.google.com/open?id=1mEnZfBKqA4eT6Bn0eRZuP6lNzL-IL3VD)
- [Single Mandarin speaker FastSpeech](https://drive.google.com/open?id=1Ol_048Tuy6BgvYm1RpjhOX4HfhUeBqdK)
- [Multi Japanese speaker Transformer](https://drive.google.com/open?id=1fFMQDF6NV5Ysz48QLFYE8fEvbAxCsMBw)
- [Single English speaker models with Parallel WaveGAN](https://drive.google.com/open?id=1HvB0_LDf1PVinJdehiuCt5gWmXGguqtx)
- [Single English speaker knowledge distillation-based FastSpeech](https://drive.google.com/open?id=1wG-Y0itVYalxuLAHdkAHO7w1CWFfRPF4)
You can download all of the pretrained models and generated samples:
- [All of the pretrained E2E-TTS models](https://drive.google.com/open?id=1k9RRyc06Zl0mM2A7mi-hxNiNMFb_YzTF)
- [All of the generated samples](https://drive.google.com/open?id=1bQGuqH92xuxOX__reWLP4-cif0cbpMLX)
Note that in the generated samples we use the following vocoders: Griffin-Lim (**GL**), WaveNet vocoder (**WaveNet**), Parallel WaveGAN (**ParallelWaveGAN**), and MelGAN (**MelGAN**).
The neural vocoders are based on following repositories.
- [kan-bayashi/ParallelWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN): Parallel WaveGAN / MelGAN / Multi-band MelGAN
- [r9y9/wavenet_vocoder](https://github.com/r9y9/wavenet_vocoder): 16 bit mixture of Logistics WaveNet vocoder
- [kan-bayashi/PytorchWaveNetVocoder](https://github.com/kan-bayashi/PytorchWaveNetVocoder): 8 bit Softmax WaveNet Vocoder with the noise shaping
If you want to build your own neural vocoder, please check the above repositories.
[kan-bayashi/ParallelWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN) provides [the manual](https://github.com/kan-bayashi/ParallelWaveGAN#decoding-with-espnet-tts-models-features) about how to decode ESPnet-TTS model's features with neural vocoders. Please check it.
Here we list all of the pretrained neural vocoders. Please download and enjoy the generation of high quality speech!
| Model link | Lang | Fs [Hz] | Mel range [Hz] | FFT / Shift / Win [pt] | Model type |
| :------ | :---: | :----: | :--------: | :---------------: | :------ |
| [ljspeech.wavenet.softmax.ns.v1](https://drive.google.com/open?id=1eA1VcRS9jzFa-DovyTgJLQ_jmwOLIi8L) | EN | 22.05k | None | 1024 / 256 / None | [Softmax WaveNet](https://github.com/kan-bayashi/PytorchWaveNetVocoder) |
| [ljspeech.wavenet.mol.v1](https://drive.google.com/open?id=1sY7gEUg39QaO1szuN62-Llst9TrFno2t) | EN | 22.05k | None | 1024 / 256 / None | [MoL WaveNet](https://github.com/r9y9/wavenet_vocoder) |
| [ljspeech.parallel_wavegan.v1](https://drive.google.com/open?id=1tv9GKyRT4CDsvUWKwH3s_OfXkiTi0gw7) | EN | 22.05k | None | 1024 / 256 / None | [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN) |
| [ljspeech.wavenet.mol.v2](https://drive.google.com/open?id=1es2HuKUeKVtEdq6YDtAsLNpqCy4fhIXr) | EN | 22.05k | 80-7600 | 1024 / 256 / None | [MoL WaveNet](https://github.com/r9y9/wavenet_vocoder) |
| [ljspeech.parallel_wavegan.v2](https://drive.google.com/open?id=1Grn7X9wD35UcDJ5F7chwdTqTa4U7DeVB) | EN | 22.05k | 80-7600 | 1024 / 256 / None | [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN) |
| [ljspeech.melgan.v1](https://drive.google.com/open?id=1ipPWYl8FBNRlBFaKj1-i23eQpW_W_YcR) | EN | 22.05k | 80-7600 | 1024 / 256 / None | [MelGAN](https://github.com/kan-bayashi/ParallelWaveGAN) |
| [ljspeech.melgan.v3](https://drive.google.com/open?id=1_a8faVA5OGCzIcJNw4blQYjfG4oA9VEt) | EN | 22.05k | 80-7600 | 1024 / 256 / None | [MelGAN](https://github.com/kan-bayashi/ParallelWaveGAN) |
| [libritts.wavenet.mol.v1](https://drive.google.com/open?id=1jHUUmQFjWiQGyDd7ZeiCThSjjpbF_B4h) | EN | 24k | None | 1024 / 256 / None | [MoL WaveNet](https://github.com/r9y9/wavenet_vocoder) |
| [jsut.wavenet.mol.v1](https://drive.google.com/open?id=187xvyNbmJVZ0EZ1XHCdyjZHTXK9EcfkK) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | [MoL WaveNet](https://github.com/r9y9/wavenet_vocoder) |
| [jsut.parallel_wavegan.v1](https://drive.google.com/open?id=1OwrUQzAmvjj1x9cDhnZPp6dqtsEqGEJM) | JP | 24k | 80-7600 | 2048 / 300 / 1200 | [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN) |
| [csmsc.wavenet.mol.v1](https://drive.google.com/open?id=1PsjFRV5eUP0HHwBaRYya9smKy5ghXKzj) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | [MoL WaveNet](https://github.com/r9y9/wavenet_vocoder) |
| [csmsc.parallel_wavegan.v1](https://drive.google.com/open?id=10M6H88jEUGbRWBmU1Ff2VaTmOAeL8CEy) | ZH | 24k | 80-7600 | 2048 / 300 / 1200 | [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN) |
If you want to use the above pretrained vocoders, please exactly match the feature setting with them.
ESPnet2
You can try the real-time demo in Google Colab.
Please access the notebook from the following button and enjoy the real-time synthesis!
- Real-time TTS demo with ESPnet2 [](https://colab.research.google.com/github/espnet/notebook/blob/master/espnet2_tts_realtime_demo.ipynb)
English, Japanese, and Mandarin models are available in the demo.
ESPnet1
> NOTE: We are moving on ESPnet2-based development for TTS. Please check the latest demo in the above ESPnet2 demo.
You can try the real-time demo in Google Colab.
Please access the notebook from the following button and enjoy the real-time synthesis.
- Real-time TTS demo with ESPnet1 [](https://colab.research.google.com/github/espnet/notebook/blob/master/tts_realtime_demo.ipynb)
We also provide shell script to perform synthesize.
Go to a recipe directory and run `utils/synth_wav.sh` as follows:
```sh
# go to recipe directory and source path of espnet tools
cd egs/ljspeech/tts1 && . ./path.sh
# we use upper-case char sequence for the default model.
echo "THIS IS A DEMONSTRATION OF TEXT TO SPEECH." > example.txt
# let's synthesize speech!
synth_wav.sh example.txt
# also you can use multiple sentences
echo "THIS IS A DEMONSTRATION OF TEXT TO SPEECH." > example_multi.txt
echo "TEXT TO SPEECH IS A TECHNIQUE TO CONVERT TEXT INTO SPEECH." >> example_multi.txt
synth_wav.sh example_multi.txt
```
You can change the pretrained model as follows:
```sh
synth_wav.sh --models ljspeech.fastspeech.v1 example.txt
```
Waveform synthesis is performed with Griffin-Lim algorithm and neural vocoders (WaveNet and ParallelWaveGAN).
You can change the pretrained vocoder model as follows:
```sh
synth_wav.sh --vocoder_models ljspeech.wavenet.mol.v1 example.txt
```
WaveNet vocoder provides very high quality speech but it takes time to generate.
See more details or available models via `--help`.
```sh
synth_wav.sh --help
```
expand
- Transformer and Tacotron2 based VC
You can listen to some samples on the [demo webpage](https://unilight.github.io/Publication-Demos/publications/transformer-vc/).
- Cascade ASR+TTS as one of the baseline systems of VCC2020
The [Voice Conversion Challenge 2020](http://www.vc-challenge.org/) (VCC2020) adopts ESPnet to build an end-to-end based baseline system.
In VCC2020, the objective is intra/cross lingual nonparallel VC.
You can download converted samples of the cascade ASR+TTS baseline system [here](https://drive.google.com/drive/folders/1oeZo83GrOgtqxGwF7KagzIrfjr8X59Ue?usp=sharing).
ESPnet2
- Transformer based SLU for Fluent Speech Command Dataset
In SLU, The objective is to infer the meaning or intent of spoken utterance. The [Fluent Speech Command Dataset](https://fluent.ai/fluent-speech-commands-a-dataset-for-spoken-language-understanding-research/) describes an intent as combination of 3 slot values: action, object and location. You can see baseline results on this dataset [here](https://github.com/espnet/espnet/blob/master/egs2/fsc/asr1/RESULTS.md)
ESPnet1
[CTC segmentation](https://arxiv.org/abs/2007.09127) determines utterance segments within audio files.
Aligned utterance segments constitute the labels of speech datasets.
As demo, we align start and end of utterances within the audio file `ctc_align_test.wav`, using the example script `utils/asr_align_wav.sh`.
For preparation, set up a data directory:
```sh
cd egs/tedlium2/align1/
# data directory
align_dir=data/demo
mkdir -p ${align_dir}
# wav file
base=ctc_align_test
wav=../../../test_utils/${base}.wav
# recipe files
echo "batchsize: 0" > ${align_dir}/align.yaml
cat << EOF > ${align_dir}/utt_text
${base} THE SALE OF THE HOTELS
${base} IS PART OF HOLIDAY'S STRATEGY
${base} TO SELL OFF ASSETS
${base} AND CONCENTRATE
${base} ON PROPERTY MANAGEMENT
EOF
```
Here, `utt_text` is the file containing the list of utterances.
Choose a pre-trained ASR model that includes a CTC layer to find utterance segments:
```sh
# pre-trained ASR model
model=wsj.transformer_small.v1
mkdir ./conf && cp ../../wsj/asr1/conf/no_preprocess.yaml ./conf
../../../utils/asr_align_wav.sh \
--models ${model} \
--align_dir ${align_dir} \
--align_config ${align_dir}/align.yaml \
${wav} ${align_dir}/utt_text
```
Segments are written to `aligned_segments` as a list of file/utterance name, utterance start and end times in seconds and a confidence score.
The confidence score is a probability in log space that indicates how good the utterance was aligned. If needed, remove bad utterances:
```sh
min_confidence_score=-5
awk -v ms=${min_confidence_score} '{ if ($5 > ms) {print} }' ${align_dir}/aligned_segments
```
The demo script `utils/ctc_align_wav.sh` uses an already pretrained ASR model (see list above for more models).
It is recommended to use models with RNN-based encoders (such as BLSTMP) for aligning large audio files;
rather than using Transformer models that have a high memory consumption on longer audio data.
The sample rate of the audio must be consistent with that of the data used in training; adjust with `sox` if needed.
A full example recipe is in `egs/tedlium2/align1/`.
ESPnet2
[CTC segmentation](https://arxiv.org/abs/2007.09127) determines utterance segments within audio files.
Aligned utterance segments constitute the labels of speech datasets.
As demo, we align start and end of utterances within the audio file `ctc_align_test.wav`.
This can be done either directly from the Python command line or using the script `espnet2/bin/asr_align.py`.
From the Python command line interface:
```python
# load a model with character tokens
from espnet_model_zoo.downloader import ModelDownloader
d = ModelDownloader(cachedir="./modelcache")
wsjmodel = d.download_and_unpack("kamo-naoyuki/wsj")
# load the example file included in the ESPnet repository
import soundfile
speech, rate = soundfile.read("./test_utils/ctc_align_test.wav")
# CTC segmentation
from espnet2.bin.asr_align import CTCSegmentation
aligner = CTCSegmentation( **wsjmodel , fs=rate )
text = """
utt1 THE SALE OF THE HOTELS
utt2 IS PART OF HOLIDAY'S STRATEGY
utt3 TO SELL OFF ASSETS
utt4 AND CONCENTRATE ON PROPERTY MANAGEMENT
"""
segments = aligner(speech, text)
print(segments)
# utt1 utt 0.26 1.73 -0.0154 THE SALE OF THE HOTELS
# utt2 utt 1.73 3.19 -0.7674 IS PART OF HOLIDAY'S STRATEGY
# utt3 utt 3.19 4.20 -0.7433 TO SELL OFF ASSETS
# utt4 utt 4.20 6.10 -0.4899 AND CONCENTRATE ON PROPERTY MANAGEMENT
```
Aligning also works with fragments of the text.
For this, set the `gratis_blank` option that allows skipping unrelated audio sections without penalty.
It's also possible to omit the utterance names at the beginning of each line, by setting `kaldi_style_text` to False.
```python
aligner.set_config( gratis_blank=True, kaldi_style_text=False )
text = ["SALE OF THE HOTELS", "PROPERTY MANAGEMENT"]
segments = aligner(speech, text)
print(segments)
# utt_0000 utt 0.37 1.72 -2.0651 SALE OF THE HOTELS
# utt_0001 utt 4.70 6.10 -5.0566 PROPERTY MANAGEMENT
```
The script `espnet2/bin/asr_align.py` uses a similar interface. To align utterances:
```sh
# ASR model and config files from pretrained model (e.g. from cachedir):
asr_config=
/config.yaml
asr_model=/valid.*best.pth
# prepare the text file
wav="test_utils/ctc_align_test.wav"
text="test_utils/ctc_align_text.txt"
cat << EOF > ${text}
utt1 THE SALE OF THE HOTELS
utt2 IS PART OF HOLIDAY'S STRATEGY
utt3 TO SELL OFF ASSETS
utt4 AND CONCENTRATE
utt5 ON PROPERTY MANAGEMENT
EOF
# obtain alignments:
python espnet2/bin/asr_align.py --asr_train_config ${asr_config} --asr_model_file ${asr_model} --audio ${wav} --text ${text}
# utt1 ctc_align_test 0.26 1.73 -0.0154 THE SALE OF THE HOTELS
# utt2 ctc_align_test 1.73 3.19 -0.7674 IS PART OF HOLIDAY'S STRATEGY
# utt3 ctc_align_test 3.19 4.20 -0.7433 TO SELL OFF ASSETS
# utt4 ctc_align_test 4.20 4.97 -0.6017 AND CONCENTRATE
# utt5 ctc_align_test 4.97 6.10 -0.3477 ON PROPERTY MANAGEMENT
```
The output of the script can be redirected to a `segments` file by adding the argument `--output segments`.
Each line contains file/utterance name, utterance start and end times in seconds and a confidence score; optionally also the utterance text.
The confidence score is a probability in log space that indicates how good the utterance was aligned. If needed, remove bad utterances:
```sh
min_confidence_score=-7
# here, we assume that the output was written to the file `segments`
awk -v ms=${min_confidence_score} '{ if ($5 > ms) {print} }' segments
```
See the module documentation for more information.
It is recommended to use models with RNN-based encoders (such as BLSTMP) for aligning large audio files;
rather than using Transformer models that have a high memory consumption on longer audio data.
The sample rate of the audio must be consistent with that of the data used in training; adjust with `sox` if needed.