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ifrz/
wav2vec2-large-xlsr-galician

Name: wav2vec2-large-xlsr-galician
Availability: InStock

automatic-speech-recognition

endpoints_compatible

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wav2vec2-large-xlsr-galician

language: gl datasets:

OpenSLR 77
mozilla-foundation common_voice_8_0 metrics:
wer tags:
audio
automatic-speech-recognition
speech
xlsr-fine-tuning-week license: apache-2.0 model-index:
name: Galician wav2vec2-large-xlsr-galician results:
- task: name: Speech Recognition type: automatic-speech-recognition dataset_1: name: OpenSLR type: openslr args: gl dataset_2: name: mozilla-foundation type: common voice args: gl metrics:
  - name: Test WER type: wer value: 7.12

Model

Fine-tuned model for Galician language

Based on the facebook/wav2vec2-large-xlsr-53 self-supervised model Fine-tune with audio labelled from OpenSLR and Mozilla Common_Voice (both datasets previously refined)

Check training metrics to see results

Testing

Make sure that the audio speech input is sampled at 16kHz (mono).

python

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

model = Wav2Vec2ForCTC.from_pretrained("ifrz/wav2vec2-large-xlsr-galician")
processor = Wav2Vec2Processor.from_pretrained("ifrz/wav2vec2-large-xlsr-galician")

# Reading taken audio clip
import librosa, torch
audio, rate = librosa.load("./gl_test_1.wav", sr = 16000)

# Taking an input value
input_values = processor(audio, sampling_rate=16_000, return_tensors = "pt", padding="longest").input_values
# Storing logits (non-normalized prediction values)
logits = model(input_values).logits
# Storing predicted ids
prediction = torch.argmax(logits, dim = -1)

# Passing the prediction to the tokenzer decode to get the transcription
transcription = processor.batch_decode(prediction)[0]
print(transcription)