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".
🎧 Listen to StreamSpeech's translated speech 🎧
💡Highlight:
- StreamSpeech achieves SOTA performance on both offline and simultaneous speech-to-speech translation.
- StreamSpeech performs streaming ASR, simultaneous speech-to-text translation and simultaneous speech-to-speech translation via an "All in One" seamless model.
- StreamSpeech can present intermediate results (i.e., ASR or translation results) during simultaneous translation, offering a more comprehensive low-latency communication experience.
- [06.17] Add Web GUI demo, now you can experience StreamSpeech in your local browser.
- [06.05] Paper, code, models and demo of StreamSpeech are available!
- 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)
demo.mov
Simultaneously provide ASR, translation, and synthesis results via a seamless model
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 |
-
Python == 3.10, PyTorch == 2.0.1, Install fairseq & SimulEval
cd fairseq pip install --editable ./ --no-build-isolation cd SimulEval pip install --editable ./
| 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] |
| Unit config | Unit size | Vocoder language | Dataset | Model |
|---|---|---|---|---|
| mHuBERT, layer 11 | 1000 | En | LJSpeech | ckpt, config |
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.
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).
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 TrueYou 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 - Follow
./preprocess_scriptsto process CVSS-C data.
Note
You can directly use the downloaded StreamSpeech model for evaluation and skip training.
- Follow
researches/ctc_unity/train_scripts/train.simul-s2st.shto train StreamSpeech for simultaneous speech-to-speech translation. - Follow
researches/ctc_unity/train_scripts/train.offline-s2st.shto train StreamSpeech for offline speech-to-speech translation. - We also provide some other StreamSpeech variants and baseline implementations.
| 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.
Follow pred.offline-s2st.sh to evaluate the offline performance of StreamSpeech on ASR, S2TT and S2ST.
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 translationagent/speech_to_text.s2tt.streamspeech.agent.py: simultaneous speech-to-text translationagent/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.
Our project page (https://ictnlp.github.io/StreamSpeech-site/) provides some translated speech generated by StreamSpeech, listen to it 🎧.
If you have any questions, please feel free to submit an issue or contact zhangshaolei20z@ict.ac.cn.
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}
}