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Massively Multilingual Speech

Massively Multilingual Speech

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About Massively Multilingual Speech

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.

Pretrained models


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

Finetuned models


ModelLanguagesDatasetModelSupported languagesMMS-1B:FL102102FLEURSdownloaddownloadMMS-1B:L11071107MMS-labdownloaddownloadMMS-1B-all1162MMS-lab + FLEURS + CV + VP + MLSdownloaddownload


  1. Download the list of iso codes of 1107 languages.
  2. 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.



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)



# LanguagesDatasetModelDictionarySupported languages126FLEURS + VL + MMS-lab-U + MMS-unlabdownloaddownloaddownload256FLEURS + VL + MMS-lab-U + MMS-unlabdownloaddownloaddownload512FLEURS + VL + MMS-lab-U + MMS-unlabdownloaddownloaddownload1024FLEURS + VL + MMS-lab-U + MMS-unlabdownloaddownloaddownload2048FLEURS + VL + MMS-lab-U + MMS-unlabdownloaddownloaddownload4017FLEURS + VL + MMS-lab-U + MMS-unlabdownloaddownloaddownload

Commands to run inference


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_1.wav"


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

$ cat dev.tsv / /path/to/audio_1 180000 /path/to/audio_2 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=

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.

``` LANG= # 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= common_eval.path='' decoding.lexicon= decoding.lmpath= \ decoding.results_path= 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.


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.


Prepare two files in this format



/ /path/to/audio1.wav /path/to/audio2.wav /path/to/audio3.wav


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


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.

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