metadata

language: fa
datasets:
  - common_voice_6_1
tags:
  - audio
  - automatic-speech-recognition
license: apache-2.0
model-index:
  - name: Sharif-wav2vec2
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice Corpus 6.1 (clean)
          type: common_voice_6_1
          config: clean
          split: test
          args:
            language: fa
        metrics:
          - name: Test WER
            type: wer
            value: 6

Sharif-wav2vec2

Prior to the usage, you may need to install the below dependencies:

pip -q install pyctcdecode
python -m pip -q install pypi-kenlm

Then you can use it with:

import tensorflow
import torchaudio
import torch
import librosa
import numpy as np
from transformers import AutoProcessor, AutoModelForCTC

processor = AutoProcessor.from_pretrained("SLPL/Sharif-wav2vec2")
model = AutoModelForCTC.from_pretrained("SLPL/Sharif-wav2vec2")




speech_array, sampling_rate = torchaudio.load("test.wav")
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(
    np.asarray(speech_array),
    sampling_rate,
    processor.feature_extractor.sampling_rate)


features = processor(
    speech_array,
    sampling_rate=processor.feature_extractor.sampling_rate,
    return_tensors="pt",
    padding=True)
input_values = features.input_values
attention_mask = features.attention_mask
with torch.no_grad():
    logits = model(input_values, attention_mask=attention_mask).logits
    prediction = processor.batch_decode(logits.numpy()).text

print(prediction[0])
# تست

The base model fine-tuned on 108 hours of Commonvoice on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.

Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli

Abstract

The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.

Usage

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

 from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
 from datasets import load_dataset
 import torch
 
 # load model and tokenizer
 processor = Wav2Vec2Processor.from_pretrained("SLPL/Sharif-wav2vec2")
 model = Wav2Vec2ForCTC.from_pretrained("SLPL/Sharif-wav2vec2")
     
 # 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.

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("facebook/wav2vec2-base-960h").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")

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"]))

Result (WER):

"clean"	"other"
3.4	8.6