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---
language: br
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Breton by Marxav
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice br
type: common_voice
args: br
metrics:
- name: Test WER
type: wer
value: 44.34
---
# Wav2Vec2-Large-XLSR-53-Breton
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
lang = "br"
test_dataset = load_dataset("common_voice", lang, split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("Marxav/wav2vec2-large-xlsr-53-breton")
model = Wav2Vec2ForCTC.from_pretrained("Marxav/wav2vec2-large-xlsr-53-breton")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
chars_to_ignore_regex = '[\\,\,\?\.\!\;\:\"\“\%\”\�\(\)\/\«\»\½\…]'
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
batch["sentence"] = re.sub("ʼ", "'", batch["sentence"])
batch["sentence"] = re.sub("’", "'", batch["sentence"])
batch["sentence"] = re.sub('‘', "'", batch["sentence"])
return batch
nb_samples = 2
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:nb_samples], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:nb_samples])
```
The above code leads to the following prediction for the first two samples:
* Prediction: ["nel ler ket dont abenn eus netra la vez ser mirc'hid evel sij", 'an eil hag egile']
* Reference: ["N'haller ket dont a-benn eus netra pa vezer nec'het evel-se ", 'An eil hag egile ']
The model can be evaluated as follows on the {language} test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
lang = 'br'
test_dataset = load_dataset("common_voice", lang, split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained('Marxav/wav2vec2-large-xlsr-53-breton2')
model = Wav2Vec2ForCTC.from_pretrained('Marxav/wav2vec2-large-xlsr-53-breton2')
model.to("cuda")
chars_to_ignore_regex = '[\\,\,\?\.\!\;\:\"\“\%\”\�\(\)\/\«\»\½\…]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
batch["sentence"] = re.sub("ʼ", "'", batch["sentence"])
batch["sentence"] = re.sub("’", "'", batch["sentence"])
batch["sentence"] = re.sub('‘', "'", batch["sentence"])
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(remove_special_characters)
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
``` |