Mms Lid 512

🚀 Massively Multilingual Speech (MMS) - Finetuned LID

This checkpoint is a model fine-tuned for speech language identification (LID), which is part of Facebook's Massive Multilingual Speech project. It can classify raw audio input into a probability distribution over 512 output classes, with each class representing a language.

🚀 Quick Start

This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 512 languages.

Let's look at a simple example.

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-512"

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 supports 512 languages, providing wide - ranging language recognition capabilities.
• Fine - tuned for LID: Specifically fine - tuned for speech language identification, ensuring high accuracy in language classification.

💻 Usage Examples

Basic Usage

from datasets import load_dataset, Audio
from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor
import torch

# Load audio samples
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"]

model_id = "facebook/mms-lid-512"
processor = AutoFeatureExtractor.from_pretrained(model_id)
model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id)

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]

Advanced Usage

# You can load multiple audio samples from different languages and process them in a loop
languages = ["en", "ar"]
for lang in languages:
    stream_data = load_dataset("mozilla-foundation/common_voice_13_0", lang, split="test", streaming=True)
    stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000))
    sample = next(iter(stream_data))["audio"]["array"]

    inputs = processor(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]
    print(f"Detected language for {lang}: {detected_lang}")

📚 Documentation

Supported Languages

This model supports 512 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 model is released under the CC - BY - NC 4.0 license.