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流式实时翻译“All in One” seamless model for offline and simultaneous speech recognition, speech translation and speech synthesis.

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StreamSpeech

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Authors: Shaolei Zhang, Qingkai Fang, Shoutao Guo, Zhengrui Ma, Min Zhang, Yang Feng*

Code for ACL 2024 paper "StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning".

StreamSpeech

🎧 Listen to StreamSpeech's translated speech 🎧

💡Highlight:

  1. StreamSpeech achieves SOTA performance on both offline and simultaneous speech-to-speech translation.
  2. StreamSpeech performs streaming ASR, simultaneous speech-to-text translation and simultaneous speech-to-speech translation via an "All in One" seamless model.
  3. StreamSpeech can present intermediate results (i.e., ASR or translation results) during simultaneous translation, offering a more comprehensive low-latency communication experience.

🔥News

⭐Features

Support 8 Tasks

  • Offline: Speech Recognition (ASR)✅, Speech-to-Text Translation (S2TT)✅, Speech-to-Speech Translation (S2ST)✅, Speech Synthesis (TTS)✅
  • Simultaneous: Streaming ASR✅, Simultaneous S2TT✅, Simultaneous S2ST✅, Real-time TTS✅ under any latency (with one model)

GUI Demo

demo.mov

Simultaneously provide ASR, translation, and synthesis results via a seamless model

Case

Speech Input: example/wavs/common_voice_fr_17301936.mp3

Transcription (ground truth): jai donc lexpérience des années passées jen dirai un mot tout à lheure

Translation (ground truth): i therefore have the experience of the passed years i'll say a few words about that later

StreamSpeech Simultaneous Offline
Speech Recognition jai donc expérience des années passé jen dirairai un mot tout à lheure jai donc lexpérience des années passé jen dirairai un mot tout à lheure
Speech-to-Text Translation i therefore have an experience of last years i will tell a word later so i have the experience in the past years i'll say a word later
Speech-to-Speech Translation
simul-s2st.mov
offline-s2st.mov
Text-to-Speech Synthesis (incrementally synthesize speech word by word)
simul-tts.mov
offline-tts.mov

⚙Requirements

  • Python == 3.10, PyTorch == 2.0.1, Install fairseq & SimulEval

    cd fairseq
    pip install --editable ./ --no-build-isolation
    cd SimulEval
    pip install --editable ./

🚀Quick Start

1. Model Download

(1) StreamSpeech Models

Language UnitY StreamSpeech (offline) StreamSpeech (simultaneous)
Fr-En unity.fr-en.pt [Huggingface] [Baidu] streamspeech.offline.fr-en.pt [Huggingface] [Baidu] streamspeech.simultaneous.fr-en.pt [Huggingface] [Baidu]
Es-En unity.es-en.pt [Huggingface] [Baidu] streamspeech.offline.es-en.pt [Huggingface] [Baidu] streamspeech.simultaneous.es-en.pt [Huggingface] [Baidu]
De-En unity.de-en.pt [Huggingface] [Baidu] streamspeech.offline.de-en.pt [Huggingface] [Baidu] streamspeech.simultaneous.de-en.pt [Huggingface] [Baidu]

(2) Unit-based HiFi-GAN Vocoder

Unit config Unit size Vocoder language Dataset Model
mHuBERT, layer 11 1000 En LJSpeech ckpt, config

2. Prepare Data and Config (only for test/inference)

(1) Config Files

Replace /data/zhangshaolei/StreamSpeech in files configs/fr-en/config_gcmvn.yaml and configs/fr-en/config_mtl_asr_st_ctcst.yaml with your local address of StreamSpeech repo.

(2) Test Data

Prepare test data following SimulEval format. example/ provides an example:

  • wav_list.txt: Each line records the path of a source speech.
  • target.txt: Each line records the reference text, e.g., target translation or source transcription (used to calculate the metrics).

3. Inference with SimulEval

Run these scripts to inference StreamSpeech on streaming ASR, simultaneous S2TT and simultaneous S2ST.

--source-segment-size: set the chunk size (millisecond) to any value to control the latency

Simultaneous Speech-to-Speech Translation

--output-asr-translation: whether to output the intermediate ASR and translated text results during simultaneous speech-to-speech translation.

export CUDA_VISIBLE_DEVICES=0

ROOT=/data/zhangshaolei/StreamSpeech # path to StreamSpeech repo
PRETRAIN_ROOT=/data/zhangshaolei/pretrain_models 
VOCODER_CKPT=$PRETRAIN_ROOT/unit-based_HiFi-GAN_vocoder/mHuBERT.layer11.km1000.en/g_00500000 # path to downloaded Unit-based HiFi-GAN Vocoder
VOCODER_CFG=$PRETRAIN_ROOT/unit-based_HiFi-GAN_vocoder/mHuBERT.layer11.km1000.en/config.json # path to downloaded Unit-based HiFi-GAN Vocoder

LANG=fr
file=streamspeech.simultaneous.${LANG}-en.pt # path to downloaded StreamSpeech model
output_dir=$ROOT/res/streamspeech.simultaneous.${LANG}-en/simul-s2st

chunk_size=320 #ms
PYTHONPATH=$ROOT/fairseq simuleval --data-bin ${ROOT}/configs/${LANG}-en \
    --user-dir ${ROOT}/researches/ctc_unity --agent-dir ${ROOT}/agent \
    --source example/wav_list.txt --target example/target.txt \
    --model-path $file \
    --config-yaml config_gcmvn.yaml --multitask-config-yaml config_mtl_asr_st_ctcst.yaml \
    --agent $ROOT/agent/speech_to_speech.streamspeech.agent.py \
    --vocoder $VOCODER_CKPT --vocoder-cfg $VOCODER_CFG --dur-prediction \
    --output $output_dir/chunk_size=$chunk_size \
    --source-segment-size $chunk_size \
    --quality-metrics ASR_BLEU  --target-speech-lang en --latency-metrics AL AP DAL StartOffset EndOffset LAAL ATD NumChunks DiscontinuitySum DiscontinuityAve DiscontinuityNum RTF \
    --device gpu --computation-aware \
    --output-asr-translation True

You should get the following outputs:

fairseq plugins loaded...
fairseq plugins loaded...
fairseq plugins loaded...
fairseq plugins loaded...
2024-06-06 09:45:46 | INFO     | fairseq.tasks.speech_to_speech | dictionary size: 1,004
import agents...
Removing weight norm...
2024-06-06 09:45:50 | INFO     | agent.tts.vocoder | loaded CodeHiFiGAN checkpoint from /data/zhangshaolei/pretrain_models/unit-based_HiFi-GAN_vocoder/mHuBERT.layer11.km1000.en/g_00500000
2024-06-06 09:45:50 | INFO     | simuleval.utils.agent | System will run on device: gpu.
2024-06-06 09:45:50 | INFO     | simuleval.dataloader | Evaluating from speech to speech.
  0%|                                                                                                                                                                              | 0/2 [00:00<?, ?it/s]
Streaming ASR: 
Streaming ASR: 
Streaming ASR: je
Simultaneous translation: i would
Streaming ASR: je voudrais
Simultaneous translation: i would like to
Streaming ASR: je voudrais soumettre
Simultaneous translation: i would like to sub
Streaming ASR: je voudrais soumettre cette
Simultaneous translation: i would like to submit
Streaming ASR: je voudrais soumettre cette idée
Simultaneous translation: i would like to submit this
Streaming ASR: je voudrais soumettre cette idée à la
Simultaneous translation: i would like to submit this idea to
Streaming ASR: je voudrais soumettre cette idée à la réflexion
Simultaneous translation: i would like to submit this idea to the
Streaming ASR: je voudrais soumettre cette idée à la réflexion de
Simultaneous translation: i would like to submit this idea to the reflection
Streaming ASR: je voudrais soumettre cette idée à la réflexion de lassemblée
Simultaneous translation: i would like to submit this idea to the reflection of
Streaming ASR: je voudrais soumettre cette idée à la réflexion de lassemblée nationale
Simultaneous translation: i would like to submit this idea to the reflection of the
Streaming ASR: je voudrais soumettre cette idée à la réflexion de lassemblée nationale
Simultaneous translation: i would like to submit this idea to the reflection of the national assembly
 50%|███████████████████████████████████████████████████████████████████████████████████                                                                                   | 1/2 [00:04<00:04,  4.08s/it]
Streaming ASR: 
Streaming ASR: 
Streaming ASR: 
Streaming ASR: 
Streaming ASR: jai donc
Simultaneous translation: i therefore
Streaming ASR: jai donc
Streaming ASR: jai donc expérience des
Simultaneous translation: i therefore have an experience
Streaming ASR: jai donc expérience des années
Streaming ASR: jai donc expérience des années passé
Simultaneous translation: i therefore have an experience of last
Streaming ASR: jai donc expérience des années passé jen
Simultaneous translation: i therefore have an experience of last years
Streaming ASR: jai donc expérience des années passé jen dirairai
Simultaneous translation: i therefore have an experience of last years i will
Streaming ASR: jai donc expérience des années passé jen dirairai un mot
Simultaneous translation: i therefore have an experience of last years i will tell a
Streaming ASR: jai donc expérience des années passé jen dirairai un mot tout à lheure
Simultaneous translation: i therefore have an experience of last years i will tell a word
Streaming ASR: jai donc expérience des années passé jen dirairai un mot tout à lheure
Simultaneous translation: i therefore have an experience of last years i will tell a word later
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:06<00:00,  3.02s/it]
2024-06-06 09:45:56 | WARNING  | simuleval.scorer.asr_bleu | Beta feature: Evaluating speech output. Faieseq is required.
2024-06-06 09:46:12 | INFO | fairseq.tasks.audio_finetuning | Using dict_path : /data/zhangshaolei/.cache/ust_asr/en/dict.ltr.txt
Transcribing predictions: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00,  1.63it/s]
2024-06-06 09:46:21 | INFO     | simuleval.sentence_level_evaluator | Results:
 ASR_BLEU       AL    AL_CA    AP  AP_CA      DAL  DAL_CA  StartOffset  StartOffset_CA  EndOffset  EndOffset_CA     LAAL  LAAL_CA      ATD   ATD_CA  NumChunks  NumChunks_CA  DiscontinuitySum  DiscontinuitySum_CA  DiscontinuityAve  DiscontinuityAve_CA  DiscontinuityNum  DiscontinuityNum_CA   RTF  RTF_CA
   15.448 1724.895 2913.508 0.425  0.776 1358.812 3137.55       1280.0        2213.906     1366.0        1366.0 1724.895 2913.508 1440.146 3389.374        9.5           9.5             110.0                110.0              55.0                 55.0                 1                    1 1.326   1.326

Logs and evaluation results are stored in $output_dir/chunk_size=$chunk_size:

$output_dir/chunk_size=$chunk_size
├── wavs/
│   ├── 0_pred.wav # generated speech
│   ├── 1_pred.wav 
│   ├── 0_pred.txt # asr transcription for ASR-BLEU tookit
│   ├── 1_pred.txt 
├── config.yaml
├── asr_transcripts.txt # ASR-BLEU transcription results
├── metrics.tsv
├── scores.tsv
├── asr_cmd.bash
└── instances.log # logs of Simul-S2ST
Simultaneous Speech-to-Text Translation
export CUDA_VISIBLE_DEVICES=0

ROOT=/data/zhangshaolei/StreamSpeech # path to StreamSpeech repo

LANG=fr
file=streamspeech.simultaneous.${LANG}-en.pt # path to downloaded StreamSpeech model
output_dir=$ROOT/res/streamspeech.simultaneous.${LANG}-en/simul-s2tt

chunk_size=320 #ms
PYTHONPATH=$ROOT/fairseq simuleval --data-bin ${ROOT}/configs/${LANG}-en \
    --user-dir ${ROOT}/researches/ctc_unity --agent-dir ${ROOT}/agent \
    --source example/wav_list.txt --target example/target.txt \
    --model-path $file \
    --config-yaml config_gcmvn.yaml --multitask-config-yaml config_mtl_asr_st_ctcst.yaml \
    --agent $ROOT/agent/speech_to_text.s2tt.streamspeech.agent.py\
    --output $output_dir/chunk_size=$chunk_size \
    --source-segment-size $chunk_size \
    --quality-metrics BLEU  --latency-metrics AL AP DAL StartOffset EndOffset LAAL ATD NumChunks RTF \
    --device gpu --computation-aware 
Streaming ASR
export CUDA_VISIBLE_DEVICES=0

ROOT=/data/zhangshaolei/StreamSpeech # path to StreamSpeech repo

LANG=fr
file=streamspeech.simultaneous.${LANG}-en.pt # path to downloaded StreamSpeech model
output_dir=$ROOT/res/streamspeech.simultaneous.${LANG}-en/streaming-asr

chunk_size=320 #ms
PYTHONPATH=$ROOT/fairseq simuleval --data-bin ${ROOT}/configs/${LANG}-en \
    --user-dir ${ROOT}/researches/ctc_unity --agent-dir ${ROOT}/agent \
    --source example/wav_list.txt --target example/source.txt \
    --model-path $file \
    --config-yaml config_gcmvn.yaml --multitask-config-yaml config_mtl_asr_st_ctcst.yaml \
    --agent $ROOT/agent/speech_to_text.asr.streamspeech.agent.py\
    --output $output_dir/chunk_size=$chunk_size \
    --source-segment-size $chunk_size \
    --quality-metrics BLEU  --latency-metrics AL AP DAL StartOffset EndOffset LAAL ATD NumChunks RTF \
    --device gpu --computation-aware 

🎈Develop Your Own StreamSpeech

1. Data Preprocess

2. Training

Note

You can directly use the downloaded StreamSpeech model for evaluation and skip training.

model

Model --user-dir --arch Description
Translatotron 2 researches/translatotron s2spect2_conformer_modified Translatotron 2
UnitY researches/translatotron unity_conformer_modified UnitY
Uni-UnitY researches/uni_unity uni_unity_conformer Change all encoders in UnitY into unidirectional
Chunk-UnitY researches/chunk_unity chunk_unity_conformer Change the Conformer in UnitY into Chunk-based Conformer
StreamSpeech researches/ctc_unity streamspeech StreamSpeech
StreamSpeech (cascade) researches/ctc_unity streamspeech_cascade Cascaded StreamSpeech of S2TT and TTS. TTS module can be used independently for real-time TTS given incremental text.
HMT researches/hmt hmt_transformer_iwslt_de_en HMT: strong simultaneous text-to-text translation method
DiSeg researches/diseg convtransformer_espnet_base_seg DiSeg: strong simultaneous speech-to-text translation method

Tip

The train_scripts/ and test_scripts/ in directory --user-dir give the training and testing scripts for each model. Refer to official repo of UnitY, Translatotron 2, HMT and DiSeg for more details.

3. Evaluation

(1) Offline Evaluation

Follow pred.offline-s2st.sh to evaluate the offline performance of StreamSpeech on ASR, S2TT and S2ST.

(2) Simultaneous Evaluation

A trained StreamSpeech model can be used for streaming ASR, simultaneous speech-to-text translation and simultaneous speech-to-speech translation. We provide agent/ for these three tasks:

  • agent/speech_to_speech.streamspeech.agent.py: simultaneous speech-to-speech translation
  • agent/speech_to_text.s2tt.streamspeech.agent.py: simultaneous speech-to-text translation
  • agent/speech_to_text.asr.streamspeech.agent.py: streaming ASR

Follow simuleval.simul-s2st.sh, simuleval.simul-s2tt.sh, simuleval.streaming-asr.sh to evaluate StreamSpeech.

4. Our Results

Our project page (https://ictnlp.github.io/StreamSpeech-site/) provides some translated speech generated by StreamSpeech, listen to it 🎧.

(1) Offline Speech-to-Speech Translation ( ASR-BLEU: quality )

offline

(2) Simultaneous Speech-to-Speech Translation ( AL: latency | ASR-BLEU: quality )

simul

(3) Simultaneous Speech-to-Text Translation ( AL: latency | BLEU: quality )

simul

(4) Streaming ASR ( AL: latency | WER: quality )

simul

🖋Citation

If you have any questions, please feel free to submit an issue or contact [email protected].

If our work is useful for you, please cite as:

@inproceedings{streamspeech,
      title={StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning}, 
      author={Shaolei Zhang and Qingkai Fang and Shoutao Guo and Zhengrui Ma and Min Zhang and Yang Feng},
      year={2024},
      booktitle = {Proceedings of the 62th Annual Meeting of the Association for Computational Linguistics (Long Papers)},
      publisher = {Association for Computational Linguistics}
}

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