README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - automatic-speech-recognition
  - pytorch
  - transformers
  - en
  - generated_from_trainer
model-index:
  - name: wav2vec2-xls-r-300m-phoneme
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: DARPA TIMIT
          type: timit
          args: en
        metrics:
          - name: Test CER
            type: cer
            value: 7.996

Model

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Timit dataset. Check this notebook for training detail.

Usage

Approach 1: Using HuggingFace's pipeline, this will cover everything end-to-end from raw audio input to text output.

from transformers import pipeline

# Load the model
pipe = pipeline(model="vitouphy/wav2vec2-xls-r-300m-timit-phoneme")
# Process raw audio
output = pipe("audio_file.wav", chunk_length_s=10, stride_length_s=(4, 2))

Approach 2: More custom way to predict phonemes.

from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC 
from datasets import load_dataset
import torch
import soundfile as sf

# load model and processor
processor = Wav2Vec2Processor.from_pretrained("vitouphy/wav2vec2-xls-r-300m-timit-phoneme")
model = Wav2Vec2ForCTC.from_pretrained("vitouphy/wav2vec2-xls-r-300m-timit-phoneme")

# Read and process the input
audio_input, sample_rate = sf.read("audio_file.wav")
inputs = processor(audio_input, sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

# Decode id into string
predicted_ids = torch.argmax(logits, axis=-1)      
predicted_sentences = processor.batch_decode(predicted_ids)
print(predicted_sentences)

Training and evaluation data

We use DARPA TIMIT dataset for this model.

• We split into 80/10/10 for training, validation, and testing respectively.
• That roughly corresponds to about 137/17/17 minutes.
• The model obtained 7.996% on this test set.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• 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: 2000
• training_steps: 10000
• mixed_precision_training: Native AMP

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.2.dev0
• Tokenizers 0.11.0