Gujarati_VDB_Fine_Tune_2

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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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Uses

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Direct Use

import torch
import librosa
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

processor = AutoProcessor.from_pretrained("iiBLACKii/Gujarati_VDB_Fine_Tune_2")
model = AutoModelForSpeechSeq2Seq.from_pretrained("iiBLACKii/Gujarati_VDB_Fine_Tune_2")

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)

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

repo_name = "iiBLACKii/Gujarati_VDB_Fine_Tune_2"

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}")

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