---
language: fa
datasets:
- common_voice_6_1
tags:
- audio
- automatic-speech-recognition
license: apache-2.0
#widget:
#- example_title: Librispeech sample 1
#  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
#- example_title: Librispeech sample 2
#  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
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: 6.0
  #- task:
   #   name: Automatic Speech Recognition
   #   type: automatic-speech-recognition
   # dataset:
   #   name: LibriSpeech (other)
   #   type: librispeech_asr
   #   config: other
   #   split: test
   #   args: 
   #     language: en
   # metrics:
   # - name: Test WER
   #   type: wer
   #   value: 8.6
---

# Sharif-wav2vec2

[Sharif-wav2vec2](https://huggingface.co/SLPL/Sharif-wav2vec2/)

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.

#[Paper](https://arxiv.org/abs/2006.11477)

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

#**Abstract**

#We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can #outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and #solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all #labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec #2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of #labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech #recognition with limited amounts of labeled data.

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:

```python
 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.
 
```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("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 |