Mms Lid 256

🚀 Massively Multilingual Speech (MMS) - Finetuned LID

This project offers a fine - tuned model for speech language identification (LID) as part of Facebook's Massive Multilingual Speech project. It can classify raw audio input into 256 different languages, providing a powerful tool for multilingual speech processing.

🚀 Quick Start

This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 256 languages.

Let's look at a simple example.

Basic Usage

First, we install transformers and some other libraries

pip install torch accelerate torchaudio datasets
pip install --upgrade transformers

Note: In order to use MMS you need to have at least transformers >= 4.30 installed. If the 4.30 version is not yet available on PyPI make sure to install transformers from source:

pip install git+https://github.com/huggingface/transformers.git

Next, we load a couple of audio samples via datasets. Make sure that the audio data is sampled to 16000 kHz.

from datasets import load_dataset, Audio

# English
stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True)
stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000))
en_sample = next(iter(stream_data))["audio"]["array"]

# Arabic
stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "ar", split="test", streaming=True)
stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000))
ar_sample = next(iter(stream_data))["audio"]["array"]

Next, we load the model and processor

from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor
import torch

model_id = "facebook/mms-lid-256"

processor = AutoFeatureExtractor.from_pretrained(model_id)
model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id)

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

# English
inputs = processor(en_sample, sampling_rate=16_000, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs).logits

lang_id = torch.argmax(outputs, dim=-1)[0].item()
detected_lang = model.config.id2label[lang_id]
# 'eng'

# Arabic
inputs = processor(ar_sample, sampling_rate=16_000, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs).logits

lang_id = torch.argmax(outputs, dim=-1)[0].item()
detected_lang = model.config.id2label[lang_id]
# 'ara'

To see all the supported languages of a checkpoint, you can print out the language ids as follows:

processor.id2label.values()

For more details, about the architecture please have a look at the official docs.

✨ Features

• Multilingual Support: This model can classify raw audio input into a probability distribution over 256 output classes, each representing a language.
• Based on Wav2Vec2: It is based on the Wav2Vec2 architecture, which provides a solid foundation for speech processing.

📦 Installation

pip install torch accelerate torchaudio datasets
pip install --upgrade transformers

If the 4.30 version of transformers is not yet available on PyPI, install it from source:

pip install git+https://github.com/huggingface/transformers.git

📚 Documentation

Supported Languages

This model supports 256 languages. Unclick the following to toogle all supported languages of this checkpoint in ISO 639 - 3 code. You can find more details about the languages and their ISO 649 - 3 codes in the MMS Language Coverage Overview.

Model details

Additional Links

• Blog post
• Transformers documentation.
• Paper
• GitHub Repository
• Other MMS checkpoints
• MMS base checkpoints:
  • facebook/mms-1b
  • facebook/mms-300m
• Official Space

📄 License

This project is licensed under the CC - BY - NC 4.0 license.