# Wav2Vec2-Large-XLSR-53-Telugu
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Telugu using the [OpenSLR SLR66](http://openslr.org/66/) 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:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import pandas as pd
# Evaluation notebook contains the procedure to download the data
df = pd.read_csv("/content/te/test.tsv", sep="\t")
df["path"] = "/content/te/clips/" + df["path"]
test_dataset = Dataset.from_pandas(df)
processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu")
model = Wav2Vec2ForCTC.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu") 
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 = 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
```python
import torch
import torchaudio
from datasets import Dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
from sklearn.model_selection import train_test_split
import pandas as pd
# Evaluation notebook contains the procedure to download the data
df = pd.read_csv("/content/te/test.tsv", sep="\t")
df["path"] = "/content/te/clips/" + df["path"]
test_dataset = Dataset.from_pandas(df)
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu")
model = Wav2Vec2ForCTC.from_pretrained("anuragshas/wav2vec2-large-xlsr-53-telugu") 
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\_\;\:\"\“\%\‘\”\।\’\'\&]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
def normalizer(text):
    # Use your custom normalizer
    text = text.replace("\\n","\n")
    text = ' '.join(text.split())
    text = re.sub(r'''([a-z]+)''','',text,flags=re.IGNORECASE)
    text = re.sub(r'''%'''," శాతం ", text)
    text = re.sub(r'''(/|-|_)'''," ", text)
    text = re.sub("ై","ై", text)
    text = text.strip()
    return text
def speech_file_to_array_fn(batch):
    batch["sentence"] = normalizer(batch["sentence"])
    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**: 44.98%
## Training
70% of the OpenSLR Telugu dataset was used for training.

Train Split of annotations is [here](https://www.dropbox.com/s/xqc0wtour7f9h4c/train.tsv)

Test Split of annotations is [here](https://www.dropbox.com/s/qw1uy63oj4qdiu4/test.tsv)

Training Data Preparation notebook can be found [here](https://colab.research.google.com/drive/1_VR1QtY9qoiabyXBdJcOI29-xIKGdIzU?usp=sharing)

Training notebook can be found[here](https://colab.research.google.com/drive/14N-j4m0Ng_oktPEBN5wiUhDDbyrKYt8I?usp=sharing)

Evaluation notebook is [here](https://colab.research.google.com/drive/1SLEvbTWBwecIRTNqpQ0fFTqmr1-7MnSI?usp=sharing)
{"language": "te", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["openslr"], "metrics": ["wer"], "model-index": [{"name": "Anurag Singh XLSR Wav2Vec2 Large 53 Telugu", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "OpenSLR te", "type": "openslr", "args": "te"}, "metrics": [{"type": "wer", "value": 44.98, "name": "Test WER"}]}]}]}
transformers pytorch jax wav2vec2 automatic-speech-recognition audio speech xlsr-fine-tuning-week te dataset:openslr license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #te #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

# Wav2Vec2-Large-XLSR-53-Telugu
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Telugu using the OpenSLR SLR66 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:

## Evaluation


Test Result: 44.98%
## Training
70% of the OpenSLR Telugu dataset was used for training.

Train Split of annotations is here

Test Split of annotations is here

Training Data Preparation notebook can be found here

Training notebook can be foundhere

Evaluation notebook is here
# Wav2Vec2-Large-XLSR-53-Telugu
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Telugu using the OpenSLR SLR66 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: ## Evaluation


Test Result: 44.98% ## Training
70% of the OpenSLR Telugu dataset was used for training.

Train Split of annotations is here

Test Split of annotations is here

Training Data Preparation notebook can be found here

Training notebook can be foundhere

Evaluation notebook is here
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #te #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 # Wav2Vec2-Large-XLSR-53-Telugu
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Telugu using the OpenSLR SLR66 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: ## Evaluation


Test Result: 44.98% ## Training
70% of the OpenSLR Telugu dataset was used for training.

Train Split of annotations is here

Test Split of annotations is here

Training Data Preparation notebook can be found here

Training notebook can be foundhere

Evaluation notebook is here
passage: TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #te #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
# Wav2Vec2-Large-XLSR-53-Telugu
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Telugu using the OpenSLR SLR66 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:## Evaluation


Test Result: 44.98%## Training
70% of the OpenSLR Telugu dataset was used for training.

Train Split of annotations is here

Test Split of annotations is here

Training Data Preparation notebook can be found here

Training notebook can be foundhere

Evaluation notebook is here