Wav2Vec2-XLSR-300m-es

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the spanish common_voice dataset thanks to the GPU credits generously given by the OVHcloud for the Speech Recognition challenge. It achieves the following results on the evaluation set

Without LM:

• Loss : 0.1900
• Wer : 0.146

With 5-gram:

• WER: 0.109
• CER: 0.036

Usage with 5-gram.

The model can be used with n-gram (n=5) included in the processor as follows.

import re
from transformers import AutoModelForCTC,Wav2Vec2ProcessorWithLM
import torch

# Loading model and processor
processor = Wav2Vec2ProcessorWithLM.from_pretrained("polodealvarado/xls-r-300m-es")
model = AutoModelForCTC.from_pretrained("polodealvarado/xls-r-300m-es")

# Cleaning characters
def remove_extra_chars(batch):
    chars_to_ignore_regex = '[^a-záéíóúñ ]'
    text = batch["translation"][target_lang]
    batch["text"] = re.sub(chars_to_ignore_regex, "", text.lower())
    return batch
    
# Preparing dataset
def prepare_dataset(batch):
    audio = batch["audio"]
    batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"],return_tensors="pt",padding=True).input_values[0]    
    with processor.as_target_processor():
        batch["labels"] = processor(batch["sentence"]).input_ids
    return batch
  

common_voice_test = load_dataset("mozilla-foundation/common_voice_8_0", "es", split="test",use_auth_token=True)
common_voice_test = common_voice_test.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"])
common_voice_test = common_voice_test.cast_column("audio", Audio(sampling_rate=16_000))        
common_voice_test = common_voice_test.map(remove_extra_chars, remove_columns=dataset.column_names)
common_voice_test = common_voice_test.map(prepare_dataset)

# Testing first sample
inputs = torch_tensor(common_voice_test[0]["input_values"])

with torch.no_grad():
    logits = model(inputs).logits

pred_ids = torch.argmax(logits, dim=-1)
text = processor.batch_decode(logits.numpy()).text
print(text) # 'bien y qué regalo vas a abrir primero'

On the other, you can execute the eval.py file for evaluation

# To use GPU: --device 0

$ python eval.py --model_id polodealvarado/xls-r-300m-es --dataset mozilla-foundation/common_voice_8_0 --config es --device 0 --split test

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0003
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 4
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.16.0.dev0
• Pytorch 1.10.1+cu102
• Datasets 1.17.1.dev0
• Tokenizers 0.11.0