Whisper Small Cv11 French

🚀 Fine-tuned whisper-small model for ASR in French

This is a fine - tuned version of the openai/whisper-small model, trained on the French dataset of mozilla - foundation/common_voice_11_0. It can predict casing and punctuation, and requires speech input to be sampled at 16Khz.

🚀 Quick Start

This model is a fine - tuned version of openai/whisper-small, trained on the mozilla - foundation/common_voice_11_0 French dataset. When using the model, ensure that your speech input is also sampled at 16Khz. This model also predicts casing and punctuation.

✨ Features

• Accurate ASR: Trained on the mozilla - foundation/common_voice_11_0 French dataset, it provides high - quality automatic speech recognition for French.
• Predict Casing and Punctuation: The model can predict casing and punctuation, which is very useful for practical applications.

📚 Documentation

Performance

Below are the WERs of the pre - trained models on the [Common Voice 9.0](https://huggingface.co/datasets/mozilla - foundation/common_voice_9_0), Multilingual LibriSpeech, Voxpopuli and Fleurs. These results are reported in the original paper.

Below are the WERs of the fine - tuned models on the [Common Voice 11.0](https://huggingface.co/datasets/mozilla - foundation/common_voice_11_0), Multilingual LibriSpeech, Voxpopuli, and Fleurs. Note that these evaluation datasets have been filtered and preprocessed to only contain French alphabet characters and are removed of punctuation outside of apostrophe. The results in the table are reported as WER (greedy search) / WER (beam search with beam width 5).

💻 Usage Examples

Basic Usage

import torch

from datasets import load_dataset
from transformers import pipeline

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Load pipeline
pipe = pipeline("automatic-speech-recognition", model="bofenghuang/whisper-small-cv11-french", device=device)

# NB: set forced_decoder_ids for generation utils
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language="fr", task="transcribe")

# Load data
ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "fr", split="test", streaming=True)
test_segment = next(iter(ds_mcv_test))
waveform = test_segment["audio"]

# Run
generated_sentences = pipe(waveform, max_new_tokens=225)["text"]  # greedy
# generated_sentences = pipe(waveform, max_new_tokens=225, generate_kwargs={"num_beams": 5})["text"]  # beam search

# Normalise predicted sentences if necessary

Advanced Usage

import torch
import torchaudio

from datasets import load_dataset
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Load model
model = AutoModelForSpeechSeq2Seq.from_pretrained("bofenghuang/whisper-small-cv11-french").to(device)
processor = AutoProcessor.from_pretrained("bofenghuang/whisper-small-cv11-french", language="french", task="transcribe")

# NB: set forced_decoder_ids for generation utils
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="fr", task="transcribe")

# 16_000
model_sample_rate = processor.feature_extractor.sampling_rate

# Load data
ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "fr", split="test", streaming=True)
test_segment = next(iter(ds_mcv_test))
waveform = torch.from_numpy(test_segment["audio"]["array"])
sample_rate = test_segment["audio"]["sampling_rate"]

# Resample
if sample_rate != model_sample_rate:
    resampler = torchaudio.transforms.Resample(sample_rate, model_sample_rate)
    waveform = resampler(waveform)

# Get feat
inputs = processor(waveform, sampling_rate=model_sample_rate, return_tensors="pt")
input_features = inputs.input_features
input_features = input_features.to(device)

# Generate
generated_ids = model.generate(inputs=input_features, max_new_tokens=225)  # greedy
# generated_ids = model.generate(inputs=input_features, max_new_tokens=225, num_beams=5)  # beam search

# Detokenize
generated_sentences = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

# Normalise predicted sentences if necessary

📄 License

This model is licensed under the apache - 2.0 license.

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