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 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
Uses
Direct Use
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)
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}")
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Training Details
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