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---
library_name: transformers
tags: [speech-to-text, transcription, Gujarati, whisper, fine-tuned]
---

# Whisper Small - Fine-tuned for Gujarati Speech-to-Text

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) for Gujarati transcription and translation tasks. It is capable of converting Gujarati speech into text, and since it is based on Whisper, it supports multilingual audio inputs. This fine-tuned model was specifically trained for improving performance on Gujarati speech data.

## Model Details

### Model Description

This model was fine-tuned on Gujarati speech data to improve transcription accuracy for audio recorded in Gujarati. It has been trained to handle diverse speech inputs, including variations in accents, backgrounds, and speech styles.

- **Developed by:** [BLACK]
- **Shared by:** [None]
- **Model type:** Speech-to-Text (Fine-tuned Whisper Model)
- **Language(s):** Gujarati
- **License:** Apache-2.0
- **Finetuned from model:** [openai/whisper-small](https://huggingface.co/openai/whisper-small)


## Uses

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### Direct Use

```python
import torch
import librosa
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

processor = AutoProcessor.from_pretrained("iiBLACKii/Gujarati_VDB_Fine_Tune")
model = AutoModelForSpeechSeq2Seq.from_pretrained("iiBLACKii/Gujarati_VDB_Fine_Tune")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def preprocess_audio(file_path, sampling_rate=16000):
    audio_array, sr = librosa.load(file_path, sr=None)
    if sr != sampling_rate:
        audio_array = librosa.resample(audio_array, orig_sr=sr, target_sr=sampling_rate)
    return audio_array

def transcribe_and_translate_audio(audio_path):
    audio_array = preprocess_audio(audio_path)

    input_features = processor(audio_array, return_tensors="pt", sampling_rate=16000).input_features

    input_features = input_features.to(device)

    with torch.no_grad():
        predicted_ids = model.generate(input_features, max_length=400, num_beams=5)

    transcription_or_translation = processor.batch_decode(predicted_ids, skip_special_tokens=True)
    return transcription_or_translation[0]

if __name__ == "__main__":
    audio_file_path = ""   # .wav file path
    print("Transcribing and Translating audio...")
    result = transcribe_and_translate_audio(audio_file_path)
    print(f"Result: {result}")

```


### Using Base Model (OpenAI)
```python

import torch
import librosa
from transformers import WhisperProcessor, WhisperForConditionalGeneration, AutoConfig

repo_name = "iiBLACKii/Gujarati_VDB_Fine_Tune"

processor = WhisperProcessor.from_pretrained(repo_name)

config = AutoConfig.from_pretrained(repo_name)  

model = WhisperForConditionalGeneration.from_pretrained(repo_name, config=config)


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def preprocess_audio(file_path, sampling_rate=16000):
    audio_array, sr = librosa.load(file_path, sr=None)
    if sr != sampling_rate:
        audio_array = librosa.resample(audio_array, orig_sr=sr, target_sr=sampling_rate)
    return audio_array

def transcribe_audio(audio_path):
    audio_array = preprocess_audio(audio_path)

    input_features = processor.feature_extractor(
        audio_array, sampling_rate=16000, return_tensors="pt"
    ).input_features

    input_features = input_features.to(device)

    with torch.no_grad():
        predicted_ids = model.generate(
            input_features,
            max_new_tokens=400,  
            num_beams=5,  
        )

    transcription = processor.tokenizer.batch_decode(predicted_ids, skip_special_tokens=True)
    return transcription[0]

if __name__ == "__main__":
    audio_file_path = "" #.wav file path
    
    print("Transcribing audio...")
    transcription = transcribe_audio(audio_file_path)
    print(f"Transcription: {transcription}")
```
### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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#### Training Hyperparameters

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#### Summary



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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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