---
library_name: transformers
tags: []
---

# Model Card for Model ID

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## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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### Model Sources [optional]

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## 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}")

```
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### 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 Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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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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## Technical Specifications [optional]

### Model Architecture and Objective

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#### Hardware

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## Citation [optional]

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## Glossary [optional]

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## Model Card Authors [optional]

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