metadata
license: apache-2.0
language: fr
library_name: transformers
thumbnail: null
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- whisper-event
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Fine-tuned whisper-large-v2 model for ASR in French
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: fr
split: test
args: fr
metrics:
- name: WER (Greedy)
type: wer
value: 8.05
- name: WER (Beam 5)
type: wer
value: 7.67
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Multilingual LibriSpeech (MLS)
type: facebook/multilingual_librispeech
config: french
split: test
args: french
metrics:
- name: WER (Greedy)
type: wer
value: 5.56
- name: WER (Beam 5)
type: wer
value: 5.28
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: VoxPopuli
type: facebook/voxpopuli
config: fr
split: test
args: fr
metrics:
- name: WER (Greedy)
type: wer
value: 11.5
- name: WER (Beam 5)
type: wer
value: 10.69
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Fleurs
type: google/fleurs
config: fr_fr
split: test
args: fr_fr
metrics:
- name: WER (Greedy)
type: wer
value: 5.42
- name: WER (Beam 5)
type: wer
value: 5.05
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: African Accented French
type: gigant/african_accented_french
config: fr
split: test
args: fr
metrics:
- name: WER (Greedy)
type: wer
value: 6.47
- name: WER (Beam 5)
type: wer
value: 5.95
Fine-tuned whisper-large-v2 model for ASR in French
This model is a fine-tuned version of openai/whisper-large-v2, 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-large-v2-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-large-v2-cv11-french").to(device)
processor = AutoProcessor.from_pretrained("bofenghuang/whisper-large-v2-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