license, language, library_name, thumbnail, tags, datasets, metrics, model-index

license	language	library_name	thumbnail	tags	datasets	metrics	model-index
apache-2.0	fr	transformers	null	automatic-speech-recognition hf-asr-leaderboard whisper-event	mozilla-foundation/common_voice_11_0	wer	name results Fine-tuned whisper-small model for ASR in French task dataset metrics name type Automatic Speech Recognition automatic-speech-recognition name type config split args Common Voice 11.0 mozilla-foundation/common_voice_11_0 fr test fr name type value WER (Greedy) wer 11.76 name type value WER (Beam 5) wer 10.99 task dataset metrics name type Automatic Speech Recognition automatic-speech-recognition name type config split args Multilingual LibriSpeech (MLS) facebook/multilingual_librispeech french test french name type value WER (Greedy) wer 9.65 name type value WER (Beam 5) wer 8.91 task dataset metrics name type Automatic Speech Recognition automatic-speech-recognition name type config split args VoxPopuli facebook/voxpopuli fr test fr name type value WER (Greedy) wer 14.45 name type value WER (Beam 5) wer 13.66 task dataset metrics name type Automatic Speech Recognition automatic-speech-recognition name type config split args Fleurs google/fleurs fr_fr test fr_fr name type value WER (Greedy) wer 10.76 name type value WER (Beam 5) wer 9.83 task dataset metrics name type Automatic Speech Recognition automatic-speech-recognition name type config split args African Accented French gigant/african_accented_french fr test fr name type value WER (Greedy) wer 10.81 name type value WER (Beam 5) wer 9.26

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Fine-tuned whisper-small model for ASR in French

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

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

Inference with 🤗 low-level APIs

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