
# Wav2Vec2-Large-XLSR-53-or 
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on odia using the [Common Voice](https://huggingface.co/datasets/common_voice)
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

test_dataset = load_dataset("common_voice", "or", split="test[:2%]") 

processor = Wav2Vec2Processor.from_pretrained("danurahul/wav2vec2-large-xlsr-or") 
model = Wav2Vec2ForCTC.from_pretrained("danurahul/wav2vec2-large-xlsr-or") 

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):
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn 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():
\tlogits = 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 odia test data of Common Voice.  

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

test_dataset = load_dataset("common_voice", "or", split="test") 
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("danurahul/wav2vec2-large-xlsr-or") 
model = Wav2Vec2ForCTC.from_pretrained("danurahul/wav2vec2-large-xlsr-or") 
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):
\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn 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):
\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

\twith torch.no_grad():
\t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

\tpred_ids = torch.argmax(logits, dim=-1)
\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
\treturn 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**: 54.6 %  

## Training

The Common Voice `train`, `validation`, and test datasets were used for training as well as prediction and testing  

The script used for training can be found [https://github.com/rahul-art/wav2vec2_or]
{"language": "or", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "odia XLSR Wav2Vec2 Large 2000", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice or", "type": "common_voice", "args": "or"}, "metrics": [{"type": "wer", "value": 54.6, "name": "Test WER"}]}]}]}
transformers pytorch jax wav2vec2 automatic-speech-recognition audio speech xlsr-fine-tuning-week or dataset:common_voice license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #or #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 


# Wav2Vec2-Large-XLSR-53-or 
Fine-tuned facebook/wav2vec2-large-xlsr-53 on odia using the Common Voice
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 odia test data of Common Voice.  



Test Result: 54.6 %  

## Training

The Common Voice 'train', 'validation', and test datasets were used for training as well as prediction and testing  

The script used for training can be found [URL
# Wav2Vec2-Large-XLSR-53-or 
Fine-tuned facebook/wav2vec2-large-xlsr-53 on odia using the Common Voice
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 odia test data of Common Voice.  



Test Result: 54.6 % ## Training

The Common Voice 'train', 'validation', and test datasets were used for training as well as prediction and testing  

The script used for training can be found [URL
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #or #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 # Wav2Vec2-Large-XLSR-53-or 
Fine-tuned facebook/wav2vec2-large-xlsr-53 on odia using the Common Voice
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 odia test data of Common Voice.  



Test Result: 54.6 % ## Training

The Common Voice 'train', 'validation', and test datasets were used for training as well as prediction and testing  

The script used for training can be found [URL
passage: TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #or #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
# Wav2Vec2-Large-XLSR-53-or 
Fine-tuned facebook/wav2vec2-large-xlsr-53 on odia using the Common Voice
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 odia test data of Common Voice.  



Test Result: 54.6 %## Training

The Common Voice 'train', 'validation', and test datasets were used for training as well as prediction and testing  

The script used for training can be found [URL