language:
- cy
- en
datasets:
- common_voice
metrics:
- wer
tags:
- automatic-speech-recognition
- speech
license: apache-2.0
model-index:
- name: wav2vec2-xlsr-ft-en-cy
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice cy
type: common_voice
args: cy
metrics:
- name: Test WER
type: wer
value: 17.70%
wav2vec2-xlsr-ft-en-cy
A speech recognition acoustic model for Welsh and English, fine-tuned from facebook/wav2vec2-large-xlsr-53 using English/Welsh balanced data derived from version 11 of their respective Common Voice datasets (https://commonvoice.mozilla.org/cy/datasets). Custom bilingual Common Voice train/dev and test splits were built using the scripts at https://github.com/techiaith/docker-commonvoice-custom-splits-builder#introduction
Source code and scripts for training wav2vec2-xlsr-ft-en-cy can be found at https://github.com/techiaith/docker-wav2vec2-cy.
Usage
The wav2vec2-xlsr-ft-en-cy 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-en-cy")
model = Wav2Vec2ForCTC.from_pretrained("techiaith/wav2vec2-xlsr-ft-en-cy")
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))
Evaluation
According to a balanced English+Welsh test set derived from Common Voice version 11, the WER of techiaith/wav2vec2-xlsr-ft-en-cy is 17.7%
However, when evaluated with language specific test sets, the model exhibits a bias to perform better with Welsh.
| Common Voice Test Set Language | WER | CER |
|---|---|---|
| EN+CY | 17.07 | 7.32 |
| EN | 27.54 | 11.6 |
| CY | 7.13 | 2.2 |