techiaith/banc-trawsgrifiadau-bangor
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How to use techiaith/wav2vec2-xlsr-53-ft-cy-en with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="techiaith/wav2vec2-xlsr-53-ft-cy-en") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("techiaith/wav2vec2-xlsr-53-ft-cy-en")
model = AutoModelForCTC.from_pretrained("techiaith/wav2vec2-xlsr-53-ft-cy-en")An acoustic encoder model for Welsh and English speech recognition, fine-tuned from facebook/wav2vec2-large-xlsr-53 using transcribed spontaneous speech from techiaith/banc-trawsgrifiadau-bangor (v24.01) as well as Welsh and English speech data derived from version 16.1 the Common Voice datasets techiaith/commonvoice_16_1_en_cy
The wav2vec2-xlsr-ft-cy-en model can be used directly as follows:
import torch
import torchaudio
import librosa
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
processor = Wav2Vec2Processor.from_pretrained("techiaith/wav2vec2-xlsr-ft-cy-en")
model = Wav2Vec2ForCTC.from_pretrained("techiaith/wav2vec2-xlsr-ft-cy-en")
audio, rate = librosa.load(audio_file, sr=16000)
inputs = processor(audio, sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
# greedy decoding
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))