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souzan/
zomodel

Name: zomodel
Availability: InStock

transformers

pytorch

wav2vec2

automatic-speech-recognition

audio

speech

patrickvonplaten/librispeech_asr_dummy

endpoints_compatible

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Fine-tuned facebook/wav2vec2-base large model for speech recognition in English

Fine-tuned facebook/wav2vec2-base on English using the train and validation splits of zodata. the dataset has 307912 transcibed voice samples, we used 6158 samples for training and 6036 samples for testing and the result for testing with WER accuracy metric is: Test WER: 0.340

When using this model, make sure that your speech input is sampled at 16kHz.

This model has been fine-tuned thanks to the GPU credits given by the kaggle

Usage

To transcribe audio files the model can be used as a standalone acoustic model as follows:

python

 from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
 from datasets import load_dataset
 import torch
 
 # load model and tokenizer
 processor = Wav2Vec2Processor.from_pretrained("souzan/zomodel")
 model = Wav2Vec2ForCTC.from_pretrained("souzan/zomodel")
     
 # load dummy dataset and read soundfiles
 ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
 
 # tokenize
 input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values  # Batch size 1
 
 # retrieve logits
 logits = model(input_values).logits
 
 # take argmax and decode
 predicted_ids = torch.argmax(logits, dim=-1)
 transcription = processor.batch_decode(predicted_ids)

Evaluation

This code snippet shows how to evaluate facebook/wav2vec2-base-960h on LibriSpeech's "clean" and "other" test data.

python

from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
from jiwer import wer


librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")

model = Wav2Vec2ForCTC.from_pretrained("souzan/zomodel").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("souzan/zomodel")

def map_to_pred(batch):
    input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values
    with torch.no_grad():
        logits = model(input_values.to("cuda")).logits

    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = processor.batch_decode(predicted_ids)
    batch["transcription"] = transcription
    return batch

result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["audio"])

print("WER:", wer(result["text"], result["transcription"]))