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---
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 | |