XLS-R-300M - Breton
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BR dataset. It achieves the following results on the evaluation set:
- Loss: NA
- Wer: NA
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
Training results
NA
Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
Evaluation Commands
- To evaluate on
mozilla-foundation/common_voice_8_0
with splittest
python eval.py --model_id infinitejoy/wav2vec2-large-xls-r-300m-breton-cv8 --dataset mozilla-foundation/common_voice_8_0 --config br --split test
- To evaluate on
speech-recognition-community-v2/dev_data
python eval.py --model_id infinitejoy/wav2vec2-large-xls-r-300m-breton-cv8 --dataset speech-recognition-community-v2/dev_data --config br --split validation --chunk_length_s 5.0 --stride_length_s 1.0
Inference With LM
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "infinitejoy/wav2vec2-large-xls-r-300m-breton-cv8"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "br", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
Eval results on Common Voice 7 "test" (WER):
NA
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Dataset used to train infinitejoy/wav2vec2-large-xls-r-300m-breton-cv8
Evaluation results
- Test WER on Common Voice 8self-reported54.855
- Test CER on Common Voice 8self-reported17.865