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---
language: hi
datasets:
- openslr_hindi
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- xlsr-hindi
license: apache-2.0
model-index:
- name: Fine-tuned Hindi XLSR Wav2Vec2 Large
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
datasets:
- name: Common Voice hi
type: common_voice
args: hi
- name: OpenSLR Hindi
url: https://www.openslr.org/resources/103/
metrics:
- name: Test WER
type: wer
value: 46.05
---
# Wav2Vec2-Large-XLSR-Hindi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hindi using OpenSLR Hindi dataset for training and Common Voice Hindi Test dataset for Evaluation. The OpenSLR Hindi data used for training was of size 10000 and it was randomly sampled. The OpenSLR train and test sets were combined and used as training data in order to increase the amount of variations. The evaluation was done on Common Voice Test set. The OpenSLR data is 8kHz and hence it was upsampled to 16kHz for training.
When using this model, make sure that your speech input is sampled at 16kHz.
*Note: This is the first iteration of the fine-tuning. Will update this model if WER improves in future experiments.*
## Test Results
| Dataset | WER |
| ------- | --- |
| Test split Common Voice Hindi | 46.055 % |
## 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", "hi", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("shiwangi27/wave2vec2-large-xlsr-hindi")
model = Wav2Vec2ForCTC.from_pretrained("shiwangi27/wave2vec2-large-xlsr-hindi")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# 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[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Hindi 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", "hi", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("shiwangi27/wave2vec2-large-xlsr-hindi")
model = Wav2Vec2ForCTC.from_pretrained("shiwangi27/wave2vec2-large-xlsr-hindi")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\�\।\']'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio 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)
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"])))
```
## Code
The Notebook used for training this model can be found at [shiwangi27/googlecolab](https://github.com/shiwangi27/googlecolab/blob/main/run_common_voice.ipynb).
I used a modified version of [run_common_voice.py](https://github.com/shiwangi27/googlecolab/blob/main/run_common_voice.py) for training.
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