|
--- |
|
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 |
|
- facebook/multilingual_librispeech |
|
- facebook/voxpopuli |
|
- google/fleurs |
|
- gigant/african_accented_french |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: Fine-tuned whisper-medium 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: 9.03 |
|
- name: WER (Beam 5) |
|
type: wer |
|
value: 8.73 |
|
- 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: 4.60 |
|
- name: WER (Beam 5) |
|
type: wer |
|
value: 4.44 |
|
- 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: 9.53 |
|
- name: WER (Beam 5) |
|
type: wer |
|
value: 9.46 |
|
- 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: 6.33 |
|
- name: WER (Beam 5) |
|
type: wer |
|
value: 5.94 |
|
- 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: 4.89 |
|
- name: WER (Beam 5) |
|
type: wer |
|
value: 4.56 |
|
--- |
|
|
|
<style> |
|
img { |
|
display: inline; |
|
} |
|
</style> |
|
|
|
 |
|
 |
|
 |
|
|
|
# Fine-tuned whisper-medium model for ASR in French |
|
|
|
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/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](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli), [Fleurs](https://huggingface.co/datasets/google/fleurs), [Multilingual TEDx](http://www.openslr.org/100), [MediaSpeech](https://www.openslr.org/108), and [African Accented French](https://huggingface.co/datasets/gigant/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](https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli) and [Fleurs](https://huggingface.co/datasets/google/fleurs). These results are reported in the original [paper](https://cdn.openai.com/papers/whisper.pdf).* |
|
|
|
| Model | Common Voice 9.0 | MLS | VoxPopuli | Fleurs | |
|
| --- | :---: | :---: | :---: | :---: | |
|
| [openai/whisper-small](https://huggingface.co/openai/whisper-small) | 22.7 | 16.2 | 15.7 | 15.0 | |
|
| [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) | 16.0 | 8.9 | 12.2 | 8.7 | |
|
| [openai/whisper-large](https://huggingface.co/openai/whisper-large) | 14.7 | 8.9 | **11.0** | **7.7** | |
|
| [openai/whisper-large-v2](https://huggingface.co/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](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli), and [Fleurs](https://huggingface.co/datasets/google/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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 |
|
``` |
