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bofenghuang/
whisper-medium-french

Name: whisper-medium-french
Availability: InStock

automatic-speech-recognition

hf-asr-leaderboard

whisper-event

mozilla-foundation/common_voice_11_0

facebook/multilingual_librispeech

facebook/voxpopuli

google/fleurs

gigant/african_accented_french

apache-2.0

model-index

endpoints_compatible

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Fine-tuned whisper-medium model for ASR in French

This model is a fine-tuned version of openai/whisper-medium, trained on a composite dataset comprising of over 2200 hours of French speech audio, using the train and the validation splits of Common Voice 11.0, Multilingual LibriSpeech, Voxpopuli, Fleurs, Multilingual TEDx, MediaSpeech, and African Accented French. When using the model make sure that your speech input is sampled at 16Khz. This model doesn't predict casing or punctuation.

Performance

Below are the WERs of the pre-trained models on the Common Voice 9.0, Multilingual LibriSpeech, Voxpopuli and Fleurs. These results are reported in the original paper.

Model	Common Voice 9.0	MLS	VoxPopuli	Fleurs
openai/whisper-small	22.7	16.2	15.7	15.0
openai/whisper-medium	16.0	8.9	12.2	8.7
openai/whisper-large	14.7	8.9	11.0	7.7
openai/whisper-large-v2	13.9	7.3	11.4	8.3

Below are the WERs of the fine-tuned models on the 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).

Model	Common Voice 11.0	MLS	VoxPopuli	Fleurs
bofenghuang/whisper-small-cv11-french	11.76 / 10.99	9.65 / 8.91	14.45 / 13.66	10.76 / 9.83
bofenghuang/whisper-medium-cv11-french	9.03 / 8.54	6.34 / 5.86	11.64 / 11.35	7.13 / 6.85
bofenghuang/whisper-medium-french	9.03 / 8.73	4.60 / 4.44	9.53 / 9.46	6.33 / 5.94
bofenghuang/whisper-large-v2-cv11-french	8.05 / 7.67	5.56 / 5.28	11.50 / 10.69	5.42 / 5.05
bofenghuang/whisper-large-v2-french	8.15 / 7.83	4.20 / 4.03	9.10 / 8.66	5.22 / 4.98

Usage

Inference with 🤗 Pipeline

python

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-medium-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

Inference with 🤗 low-level APIs

python

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-medium-french").to(device)
processor = AutoProcessor.from_pretrained("bofenghuang/whisper-medium-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