MMS: Scaling Speech Technology to 1000+ languages

The Massively Multilingual Speech (MMS) project expands speech technology from about 100 languages to over 1,000 by building a single multilingual speech recognition model supporting over 1,100 languages (more than 10 times as many as before), language identification models able to identify over 4,000 languages (40 times more than before), pretrained models supporting over 1,400 languages, and text-to-speech models for over 1,100 languages. Our goal is to make it easier for people to access information and to use devices in their preferred language.

You can find details in the paper Scaling Speech Technology to 1000+ languages and the blog post.

An overview of the languages covered by MMS can be found here.

🤗 Transformers

MMS has been added to Transformers. For more information, please refer to Transformers' MMS docs.

Click here to find all MMS checkpoints on the Hub.

Checkout the demo here

Finetuned models

ASR

Model	Languages	Dataset	Model	Dictionary*	Supported languages
MMS-1B:FL102	102	FLEURS	download	download	download	🤗 Hub
MMS-1B:L1107	1107	MMS-lab	download	download	download	🤗 Hub
MMS-1B-all	1162	MMS-lab + FLEURS + CV + VP + MLS	download	download	download	🤗 Hub

* In the Dictionary column, we provide the download link for token dictionary in English language. To download token dictionary for a different language supported by the model, modify the language code in the URL appropriately. For example, to get token dictionary of FL102 model for Hindi language, use this link.

TTS

Download the list of iso codes of 1107 languages.
Find the iso code of the target language and download the checkpoint. Each folder contains 3 files: G_100000.pth, config.json, vocab.txt. The G_100000.pth is the generator trained for 100K updates, config.json is the training config, vocab.txt is the vocabulary for the TTS model.

# Examples:
wget https://dl.fbaipublicfiles.com/mms/tts/eng.tar.gz # English (eng)
wget https://dl.fbaipublicfiles.com/mms/tts/azj-script_latin.tar.gz # North Azerbaijani (azj-script_latin)

The above command downloads generator only, which is enough to run TTS inference. If you want the full model checkpoint which also includes the discriminator (D_100000.pth) and the optimizer states, download as follows.

# Example (full checkpoint: generator + discriminator + optimizer):
wget https://dl.fbaipublicfiles.com/mms/tts/full_model/eng.tar.gz # English (eng)

LID

# Languages	Dataset	Model	Dictionary	Supported languages
126	FLEURS + VL + MMS-lab-U + MMS-unlab	download	download	download	🤗 Hub
256	FLEURS + VL + MMS-lab-U + MMS-unlab	download	download	download	🤗 Hub
512	FLEURS + VL + MMS-lab-U + MMS-unlab	download	download	download	🤗 Hub
1024	FLEURS + VL + MMS-lab-U + MMS-unlab	download	download	download	🤗 Hub
2048	FLEURS + VL + MMS-lab-U + MMS-unlab	download	download	download	🤗 Hub
4017	FLEURS + VL + MMS-lab-U + MMS-unlab	download	download	download	🤗 Hub

Commands to run inference

ASR

Run this command to transcribe one or more audio files:

cd /path/to/fairseq-py/
python examples/mms/asr/infer/mms_infer.py --model "/path/to/asr/model" --lang lang_code \
  --audio "/path/to/audio_1.wav" "/path/to/audio_2.wav" "/path/to/audio_3.wav"

We also provide an Ipython notebook example inside asr/tutorial folder ipynb or

For more advance configuration and calculate CER/WER, you could prepare manifest folder by creating a folder with this format:

$ ls /path/to/manifest
dev.tsv
dev.wrd
dev.ltr
dev.uid

# dev.tsv each line contains <audio>  <number_of_sample>
# if user don't have this information, please run misc/get_sample_size.py

$ cat dev.tsv
/
/path/to/audio_1.wav  180000
/path/to/audio_2.wav  200000

$ cat dev.ltr
t h i s | i s | o n e |
t h i s | i s | t w o |

$ cat dev.wrd
this is one
this is two

$ cat dev.uid
audio_1
audio_2

Followed by command below:

lang_code=<iso_code>

PYTHONPATH=. PREFIX=INFER HYDRA_FULL_ERROR=1 python examples/speech_recognition/new/infer.py -m --config-dir examples/mms/config/ --config-name infer_common decoding.type=viterbi dataset.max_tokens=4000000 distributed_training.distributed_world_size=1 "common_eval.path='/path/to/asr/model'" task.data='/path/to/manifest' dataset.gen_subset="${lang_code}:dev" common_eval.post_process=letter

Available options:

To get the raw character-based output, user can change to common_eval.post_process=none
To maximize GPU efficiency or avoid out-of-memory (OOM), user can tune dataset.max_tokens=??? size

To run language model decoding, install flashlight python bindings using

git clone --recursive git@github.com:flashlight/flashlight.git
cd flashlight; 
git checkout 035ead6efefb82b47c8c2e643603e87d38850076 
cd bindings/python 
python3 setup.py install

Train a KenLM language model and prepare a lexicon file in this format. Pretrained languages models from our paper can be found in 🤗 Hub.

 LANG=<iso> # for example - 'eng', 'azj-script_latin'
 PYTHONPATH=. PREFIX=INFER HYDRA_FULL_ERROR=1  python examples/speech_recognition/new/infer.py  --config-dir=examples/mms/asr/config \
    --config-name=infer_common decoding.type=kenlm  distributed_training.distributed_world_size=1  \ 
    decoding.unique_wer_file=true   decoding.beam=500 decoding.beamsizetoken=50  \
    task.data=<MANIFEST_FOLDER_PATH>   common_eval.path='<MODEL_PATH.pt>' decoding.lexicon=<LEXICON_FILE> decoding.lmpath=<LM_FILE> \  
    decoding.results_path=<OUTPUT_DIR> dataset.gen_subset=${LANG}:dev decoding.lmweight=??? decoding.wordscore=???

We typically sweep lmweight in the range of 0 to 5 and wordscore in the range of -3 to 3. The output directory will contain the reference and hypothesis outputs from decoder.

For decoding with character-based language models, use empty lexicon file (decoding.lexicon=), decoding.unitlm=True and sweep over decoding.silweight instead of wordscore.

TTS

Note: clone and install VITS before running inference.

## English TTS
$ PYTHONPATH=$PYTHONPATH:/path/to/vits python examples/mms/tts/infer.py --model-dir /path/to/model/eng \
--wav ./example.wav --txt "Expanding the language coverage of speech technology \
has the potential to improve access to information for many more people"

## Maithili TTS
$ PYTHONPATH=$PYTHONPATH:/path/to/vits python examples/mms/tts/infer.py --model-dir /path/to/model/mai \
--wav ./example.wav --txt "मुदा आइ धरि ई तकनीक सौ सं किछु बेसी भाषा तक सीमित छल जे सात हजार \ 
सं बेसी ज्ञात भाषाक एकटा अंश अछी"

example.wav contains synthesized audio for the language.

We also provide an Ipython notebook example inside tts/tutorial folder ipynb or

LID

Prepare two files in this format. Each manifest line contains and <number_of_sample>

#/path/to/manifest.tsv
/
/path/to/audio1.wav	180000
/path/to/audio2.wav	240000
/path/to/audio3.wav	160000

# /path/to/manifest.lang
eng 1
eng 1
eng 1

Download model and the corresponding dictionary file for the LID model. Use the following command to run inference -

$  PYTHONPATH='.'  python3  examples/mms/lid/infer.py /path/to/dict/l126/ --path /path/to/models/mms1b_l126.pt \
  --task audio_classification  --infer-manifest /path/to/manifest.tsv --output-path <OUTDIR>

The above command assumes there is a file named dict.lang.txt in /path/to/dict/l126/. <OUTDIR>/predictions.txt will contain the predictions from the model for the audio files in manifest.tsv.

We also provide an Ipython notebook example inside lid/tutorial folder ipynb or

Fine-tuning

ASR

MMS Adapter fine-tuning has been added to the official 🤗 Transformers examples here. For a more step-by-step explanation of how to fine-tune MMS, please have a look at the blog Fine-tuning MMS Adapter Models for Multi-Lingual ASR on 🤗 blogs.

Pretrained models

Model	Link
MMS-300M	download	🤗 Hub
MMS-1B	download	🤗 Hub

Example commands to finetune the pretrained models can be found here.

Forced Alignment Tooling

We also developed an efficient forced alignment algorithm implemented on GPU which is able to process very long audio files. This algorithm is open sourced and we provide instructions on how to use it here. We also open source a multilingual alignment model trained on 31K hours of data in 1,130 languages, as well as text normalization scripts.

License

The MMS code and model weights are released under the CC-BY-NC 4.0 license.

Citation

BibTeX:

@article{pratap2023mms,
  title={Scaling Speech Technology to 1,000+ Languages},
  author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli},
  journal={arXiv},
  year={2023}
}

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README.md

README.md

MMS: Scaling Speech Technology to 1000+ languages

🤗 Transformers

Finetuned models

ASR

TTS

LID

Commands to run inference

ASR

TTS

LID

Fine-tuning

ASR

Pretrained models

Forced Alignment Tooling

License

Citation

Files

README.md

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README.md

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MMS: Scaling Speech Technology to 1000+ languages

🤗 Transformers

Finetuned models

ASR

TTS

LID

Commands to run inference

ASR

TTS

LID

Fine-tuning

ASR

Pretrained models

Forced Alignment Tooling

License

Citation