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
- 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:
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)
