
# Wav2Vec2-Large-XLSR-53-Marathi

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using a part of the [InterSpeech 2021 Marathi](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html) dataset. 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, assuming you have a dataset with Marathi `sentence` and `path` fields:

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

# test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section.

processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-2")
model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-2")

resampler = torchaudio.transforms.Resample(8_000, 16_000) # The original data was with 8,000 sampling rate. You can change it according to your input.

# Preprocessing the datasets.
# We need to read the audio 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 = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

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

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

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation

The model can be evaluated as follows on the test set of the Marathi data on InterSpeech-2021.

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

# test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section.

wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-2")
model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-2")
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\�]'
resampler = torchaudio.transforms.Resample(8_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 = test_dataset.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=16_000, return_tensors="pt", padding=True)
    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), 
        attention_mask=inputs.attention_mask.to("cuda")).logits
        pred_ids = torch.argmax(logits, dim=-1)
        batch["pred_strings"] = processor.batch_decode(pred_ids)
        return batch

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

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

**Test Result**: 19.98 %  (555 examples from test set were used for evaluation)

**Test Result on 10% of OpenSLR74 data**: 64.64 %

## Training

5000 examples of the InterSpeech Marathi dataset were used for training. 
The colab notebook used for training can be found [here](https://colab.research.google.com/drive/1sIwGOLJPQqhKm_wVZDkzRuoJqAEgArFr?usp=sharing). 

{"language": "mr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["interspeech_2021_asr"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 Marathi 2 by Gunjan Chhablani", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "InterSpeech 2021 ASR mr", "type": "interspeech_2021_asr"}, "metrics": [{"type": "wer", "value": 14.53, "name": "Test WER"}]}]}]}
transformers pytorch jax wav2vec2 automatic-speech-recognition audio speech xlsr-fine-tuning-week mr dataset:interspeech_2021_asr license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mr #dataset-interspeech_2021_asr #license-apache-2.0 #model-index #endpoints_compatible #region-us 


# Wav2Vec2-Large-XLSR-53-Marathi

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi using a part of the InterSpeech 2021 Marathi dataset. 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, assuming you have a dataset with Marathi 'sentence' and 'path' fields:




## Evaluation

The model can be evaluated as follows on the test set of the Marathi data on InterSpeech-2021.



Test Result: 19.98 %  (555 examples from test set were used for evaluation)

Test Result on 10% of OpenSLR74 data: 64.64 %

## Training

5000 examples of the InterSpeech Marathi dataset were used for training. 
The colab notebook used for training can be found here. 

# Wav2Vec2-Large-XLSR-53-Marathi

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi using a part of the InterSpeech 2021 Marathi dataset. 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, assuming you have a dataset with Marathi 'sentence' and 'path' fields: ## Evaluation

The model can be evaluated as follows on the test set of the Marathi data on InterSpeech-2021.



Test Result: 19.98 %  (555 examples from test set were used for evaluation)

Test Result on 10% of OpenSLR74 data: 64.64 % ## Training

5000 examples of the InterSpeech Marathi dataset were used for training. 
The colab notebook used for training can be found here.
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mr #dataset-interspeech_2021_asr #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 # Wav2Vec2-Large-XLSR-53-Marathi

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi using a part of the InterSpeech 2021 Marathi dataset. 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, assuming you have a dataset with Marathi 'sentence' and 'path' fields: ## Evaluation

The model can be evaluated as follows on the test set of the Marathi data on InterSpeech-2021.



Test Result: 19.98 %  (555 examples from test set were used for evaluation)

Test Result on 10% of OpenSLR74 data: 64.64 % ## Training

5000 examples of the InterSpeech Marathi dataset were used for training. 
The colab notebook used for training can be found here.
passage: TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mr #dataset-interspeech_2021_asr #license-apache-2.0 #model-index #endpoints_compatible #region-us 
# Wav2Vec2-Large-XLSR-53-Marathi

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi using a part of the InterSpeech 2021 Marathi dataset. 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, assuming you have a dataset with Marathi 'sentence' and 'path' fields:## Evaluation

The model can be evaluated as follows on the test set of the Marathi data on InterSpeech-2021.



Test Result: 19.98 %  (555 examples from test set were used for evaluation)

Test Result on 10% of OpenSLR74 data: 64.64 %## Training

5000 examples of the InterSpeech Marathi dataset were used for training. 
The colab notebook used for training can be found here.