wav2vec2-large-xlsr-galician

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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).

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)