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--- |
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tags: |
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- mms |
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language: |
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- ab |
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- af |
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- ak |
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- am |
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- ar |
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- as |
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- av |
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- ay |
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- az |
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- ba |
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- bm |
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- be |
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- bn |
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- bi |
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- bo |
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- sh |
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- br |
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- bg |
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- ca |
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- cs |
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- ce |
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- cv |
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- ku |
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- cy |
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- da |
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- de |
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- dv |
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- dz |
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- el |
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- en |
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- eo |
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- et |
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- eu |
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- ee |
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- fo |
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- fa |
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- fj |
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- fi |
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- fr |
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- fy |
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- ff |
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- ga |
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- gl |
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- gn |
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- gu |
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- zh |
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- ht |
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- ha |
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- he |
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- hi |
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- sh |
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- hu |
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- hy |
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- ig |
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- ia |
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- ms |
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- is |
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- it |
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- jv |
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- ja |
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- kn |
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- ka |
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- kk |
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- kr |
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- km |
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- ki |
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- rw |
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- ky |
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- ko |
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- kv |
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- lo |
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- la |
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- lv |
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- ln |
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- lt |
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- lb |
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- lg |
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- mh |
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- ml |
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- mr |
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- ms |
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- mk |
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- mg |
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- mt |
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- mn |
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- mi |
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- my |
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- zh |
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- nl |
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- 'no' |
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- 'no' |
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- ne |
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- ny |
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- oc |
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- om |
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- or |
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- os |
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- pa |
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- pl |
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- pt |
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- ms |
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- ps |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- ro |
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- rn |
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- ru |
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- sg |
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- sk |
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- sl |
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- sm |
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- sn |
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- sd |
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- so |
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- es |
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- sq |
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- su |
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- sv |
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- sw |
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- ta |
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- tt |
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- te |
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- tg |
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- tl |
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- th |
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- ti |
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- ts |
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- tr |
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- uk |
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- ms |
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- vi |
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- wo |
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- xh |
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- ms |
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- yo |
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- ms |
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- zu |
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- za |
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license: cc-by-nc-4.0 |
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datasets: |
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- google/fleurs |
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metrics: |
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- acc |
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--- |
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|
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# Massively Multilingual Speech (MMS) - Finetuned LID |
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This checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's [Massive Multilingual Speech project](https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/). |
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This checkpoint is based on the [Wav2Vec2 architecture](https://huggingface.co/docs/transformers/model_doc/wav2vec2) and classifies raw audio input to a probability distribution over 256 output classes (each class representing a language). |
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The checkpoint consists of **1 billion parameters** and has been fine-tuned from [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) on 256 languages. |
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|
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## Table Of Content |
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|
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- [Example](#example) |
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- [Supported Languages](#supported-languages) |
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- [Model details](#model-details) |
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- [Additional links](#additional-links) |
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|
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## Example |
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This MMS checkpoint can be used with [Transformers](https://github.com/huggingface/transformers) to identify |
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the spoken language of an audio. It can recognize the [following 256 languages](#supported-languages). |
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Let's look at a simple example. |
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|
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First, we install transformers and some other libraries |
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``` |
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pip install torch accelerate torchaudio datasets |
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pip install --upgrade transformers |
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```` |
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**Note**: In order to use MMS you need to have at least `transformers >= 4.30` installed. If the `4.30` version |
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is not yet available [on PyPI](https://pypi.org/project/transformers/) make sure to install `transformers` from |
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source: |
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``` |
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pip install git+https://github.com/huggingface/transformers.git |
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``` |
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Next, we load a couple of audio samples via `datasets`. Make sure that the audio data is sampled to 16000 kHz. |
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```py |
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from datasets import load_dataset, Audio |
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# English |
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stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True) |
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stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) |
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en_sample = next(iter(stream_data))["audio"]["array"] |
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|
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# Arabic |
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stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "ar", split="test", streaming=True) |
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stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000)) |
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ar_sample = next(iter(stream_data))["audio"]["array"] |
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``` |
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Next, we load the model and processor |
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|
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```py |
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from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor |
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import torch |
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model_id = "facebook/mms-lid-256" |
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processor = AutoFeatureExtractor.from_pretrained(model_id) |
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model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id) |
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``` |
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Now we process the audio data, pass the processed audio data to the model to classify it into a language, just like we usually do for Wav2Vec2 audio classification models such as [ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition](https://huggingface.co/harshit345/xlsr-wav2vec-speech-emotion-recognition) |
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```py |
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# English |
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inputs = processor(en_sample, sampling_rate=16_000, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs).logits |
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lang_id = torch.argmax(outputs, dim=-1)[0].item() |
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detected_lang = model.config.id2label[lang_id] |
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# 'eng' |
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# Arabic |
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inputs = processor(ar_sample, sampling_rate=16_000, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs).logits |
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lang_id = torch.argmax(outputs, dim=-1)[0].item() |
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detected_lang = model.config.id2label[lang_id] |
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# 'ara' |
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``` |
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To see all the supported languages of a checkpoint, you can print out the language ids as follows: |
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```py |
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processor.id2label.values() |
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``` |
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For more details, about the architecture please have a look at [the official docs](https://huggingface.co/docs/transformers/main/en/model_doc/mms). |
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## Supported Languages |
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This model supports 256 languages. Unclick the following to toogle all supported languages of this checkpoint in [ISO 639-3 code](https://en.wikipedia.org/wiki/ISO_639-3). |
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You can find more details about the languages and their ISO 649-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html). |
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<details> |
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<summary>Click to toggle</summary> |
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|
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- ara |
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- cmn |
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- eng |
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- spa |
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- fra |
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- mlg |
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- swe |
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- por |
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- vie |
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- ful |
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- sun |
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- asm |
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- ben |
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- zlm |
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- kor |
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- ind |
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- hin |
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- tuk |
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- urd |
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- aze |
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- slv |
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- mon |
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- hau |
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- tel |
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- swh |
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- bod |
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- rus |
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- tur |
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- heb |
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- mar |
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- som |
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- tgl |
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- tat |
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- tha |
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- cat |
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- ron |
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- mal |
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- bel |
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- pol |
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- yor |
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- nld |
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- bul |
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- hat |
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- afr |
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- isl |
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- amh |
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- tam |
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- hun |
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- hrv |
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- lit |
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- cym |
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- fas |
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- mkd |
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- ell |
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- bos |
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- deu |
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- sqi |
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- jav |
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- kmr |
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- nob |
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- uzb |
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- snd |
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- lat |
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- nya |
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- grn |
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- mya |
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- orm |
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- lin |
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- hye |
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- yue |
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- pan |
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- jpn |
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- kaz |
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- npi |
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- kik |
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- kat |
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- guj |
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- kan |
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- tgk |
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- ukr |
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- ces |
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- lav |
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- bak |
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- khm |
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- fao |
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- glg |
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- ltz |
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- xog |
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- lao |
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- mlt |
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- sin |
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- aka |
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- sna |
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- ita |
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- srp |
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- mri |
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- nno |
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- pus |
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- eus |
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- ory |
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- lug |
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- bre |
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- luo |
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- slk |
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- ewe |
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- fin |
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- rif |
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- dan |
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- yid |
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- yao |
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- mos |
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- hne |
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- est |
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- dyu |
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- bam |
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- uig |
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- sck |
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- tso |
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- mup |
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- ctg |
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- ceb |
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- war |
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- bbc |
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- vmw |
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- sid |
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- tpi |
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- mag |
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- san |
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- kri |
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- lon |
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- kir |
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- run |
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- ubl |
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- kin |
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- rkt |
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- xmm |
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- tir |
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- mai |
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- nan |
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- nyn |
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- bcc |
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- hak |
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- suk |
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- bem |
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- rmy |
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- awa |
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- pcm |
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- bgc |
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- shn |
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- oci |
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- wol |
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- bci |
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- kab |
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- ilo |
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- bcl |
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- haw |
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- mad |
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- nod |
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- sag |
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- sas |
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- jam |
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- mey |
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- shi |
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- hil |
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- ace |
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- kam |
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- min |
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- umb |
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- hno |
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- ban |
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- syl |
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- bxg |
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- xho |
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- mww |
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- epo |
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- tzm |
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- zul |
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- ibo |
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- abk |
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- guz |
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- ckb |
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- knc |
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- nso |
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- bho |
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- dje |
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- tiv |
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- gle |
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- lua |
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- skr |
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- bto |
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- kea |
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- glk |
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- ast |
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- sat |
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- ktu |
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- bhb |
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- emk |
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- kng |
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- kmb |
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- tsn |
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- gom |
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- ven |
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- sco |
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- glv |
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- sot |
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- sou |
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- gno |
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- nde |
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- bjn |
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- ina |
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- fmu |
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- esg |
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- wes |
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- pnb |
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- phr |
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- mui |
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- bug |
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- mrr |
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- kas |
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- lir |
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- vah |
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- ssw |
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- rwr |
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- pcc |
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- hms |
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- wbr |
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- swv |
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- mtr |
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- haz |
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- aii |
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- bns |
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- msi |
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- wuu |
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- hsn |
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- bgp |
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- tts |
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- lmn |
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- dcc |
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- bew |
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- bjj |
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- ibb |
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- tji |
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- hoj |
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- cpx |
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- cdo |
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- daq |
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- mut |
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- nap |
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- czh |
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- gdx |
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- sdh |
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- scn |
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- mnp |
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- bar |
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- mzn |
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- gsw |
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</details> |
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## Model details |
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|
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- **Developed by:** Vineel Pratap et al. |
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- **Model type:** Multi-Lingual Automatic Speech Recognition model |
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- **Language(s):** 256 languages, see [supported languages](#supported-languages) |
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- **License:** CC-BY-NC 4.0 license |
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- **Num parameters**: 1 billion |
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- **Audio sampling rate**: 16,000 kHz |
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- **Cite as:** |
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@article{pratap2023mms, |
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title={Scaling Speech Technology to 1,000+ Languages}, |
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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}, |
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journal={arXiv}, |
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year={2023} |
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} |
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|
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## Additional Links |
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|
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- [Blog post](https://ai.facebook.com/blog/multilingual-model-speech-recognition/) |
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- [Transformers documentation](https://huggingface.co/docs/transformers/main/en/model_doc/mms). |
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- [Paper](https://arxiv.org/abs/2305.13516) |
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- [GitHub Repository](https://github.com/facebookresearch/fairseq/tree/main/examples/mms#asr) |
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- [Other **MMS** checkpoints](https://huggingface.co/models?other=mms) |
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- MMS base checkpoints: |
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- [facebook/mms-1b](https://huggingface.co/facebook/mms-1b) |
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- [facebook/mms-300m](https://huggingface.co/facebook/mms-300m) |
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- [Official Space](https://huggingface.co/spaces/facebook/MMS) |
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|
