
# wav2vec2-base-gujarati-demo

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Guj
When using this model, make sure that your speech input is sampled at 16kHz.

## Usage

The model can be used directly (without a language model) as follows:

```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

common_voice_train,common_voice_test = load_dataset('csv', data_files={'train': 'train.csv','test': 'test.csv'},error_bad_lines=False,encoding='utf-8',split=['train', 'test']).

processor = Wav2Vec2Processor.from_pretrained("jaimin/wav2vec2-base-gujarati-demo")
model = Wav2Vec2ForCTC.from_pretrained("jaimin/wav2vec2-base-gujarati-demo")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = common_voice_test.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

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

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][0].lower())
```


## Evaluation

The model can be evaluated as follows on the {language} test data of Common Voice.


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

common_voice_validation = load_dataset('csv', data_files={'test': 'validation.csv'},error_bad_lines=False,encoding='utf-8',split='test')
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("jaimin/wav2vec2-base-gujarati-demo")
model = Wav2Vec2ForCTC.from_pretrained("Amrrs/wav2vec2-base-gujarati-demo")
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = common_voice_validation.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda")).logits
    
    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = common_voice_validation.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```

**Test Result**: 28.92 %


## Training

The Google datasets were used for training.

The script used for training can be found [here](https://colab.research.google.com/drive/1-Klkgr4f-C9SanHfVC5RhP0ELUH6TYlN?usp=sharing)

{"language": "Guj", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["google"], "model-index": [{"name": "XLSR Wav2Vec2 Guj by Jaimin", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Google", "type": "voice", "args": "guj"}, "metrics": [{"type": "wer", "value": 28.92, "name": "Test WER"}]}]}]}
transformers pytorch jax wav2vec2 automatic-speech-recognition audio speech xlsr-fine-tuning-week dataset:google license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-google #license-apache-2.0 #model-index #endpoints_compatible #region-us 


# wav2vec2-base-gujarati-demo

Fine-tuned facebook/wav2vec2-large-xlsr-53 in Guj
When using this model, make sure that your speech input is sampled at 16kHz.

## Usage

The model can be used directly (without a language model) as follows:




## Evaluation

The model can be evaluated as follows on the {language} test data of Common Voice.




Test Result: 28.92 %


## Training

The Google datasets were used for training.

The script used for training can be found here

# wav2vec2-base-gujarati-demo

Fine-tuned facebook/wav2vec2-large-xlsr-53 in Guj
When using this model, make sure that your speech input is sampled at 16kHz. ## Usage

The model can be used directly (without a language model) as follows: ## Evaluation

The model can be evaluated as follows on the {language} test data of Common Voice.




Test Result: 28.92 % ## Training

The Google datasets were used for training.

The script used for training can be found here
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-google #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 # wav2vec2-base-gujarati-demo

Fine-tuned facebook/wav2vec2-large-xlsr-53 in Guj
When using this model, make sure that your speech input is sampled at 16kHz. ## Usage

The model can be used directly (without a language model) as follows: ## Evaluation

The model can be evaluated as follows on the {language} test data of Common Voice.




Test Result: 28.92 % ## Training

The Google datasets were used for training.

The script used for training can be found here
passage: TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #dataset-google #license-apache-2.0 #model-index #endpoints_compatible #region-us 
# wav2vec2-base-gujarati-demo

Fine-tuned facebook/wav2vec2-large-xlsr-53 in Guj
When using this model, make sure that your speech input is sampled at 16kHz.## Usage

The model can be used directly (without a language model) as follows:## Evaluation

The model can be evaluated as follows on the {language} test data of Common Voice.




Test Result: 28.92 %## Training

The Google datasets were used for training.

The script used for training can be found here