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import os
import soundfile as sf # Import soundfile for audio file handling
import numpy as np
import gradio as gr
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
# Choose a suitable Kannada speech-to-text model from Hugging Face
model_name = "vasista22/whisper-kannada-tiny" # Replace with your preferred model
processor = Wav2Vec2Processor.from_pretrained(model_name)
model = Wav2Vec2ForCTC.from_pretrained(model_name)
def transcribe_kannada(audio_data):
"""
Transcribes recorded Kannada audio using the specified Hugging Face model.
Args:
audio_data: A NumPy array representing the recorded audio data.
Returns:
The transcribed text in Kannada.
"""
sampling_rate = 16000 # Assuming common speech sampling rate (adjust if needed)
audio_input = processor(audio_data, sampling_rate=sampling_rate, return_tensors="pt")
with torch.no_grad():
logits = model(**audio_input).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
return transcription
def record_and_transcribe(audio):
"""
Records audio from the microphone, processes each channel independently (if applicable),
converts them to speech-to-text, and plays reversed audio.
Args:
audio: A tuple containing recorded audio information (multiple audio channels).
Returns:
A list of transcriptions (one for each channel), or a tuple with transcriptions and reversed audio.
"""
transcriptions = []
for channel in audio:
# Process each audio channel (replace with your actual conversion logic)
audio_data = channel # Assuming no processing needed for individual channels
transcription = transcribe_kannada(audio_data)
transcriptions.append(transcription)
# ... (handle reversed audio if needed)
return transcriptions # Or a tuple with transcriptions and reversed audio
# input_audio = gr.Audio(
# sources=["microphone"],
# type="numpy", # Specify audio format as NumPy array
# normalization=" [-1, 1]", # Normalize audio data to -1 to 1 range for model compatibility
# label="Record Kannada Audio",
# )
input_audio = gr.Audio(
sources=["microphone"],
type="numpy", # Specify audio format as NumPy array
label="Record Kannada Audio",
)
text_output = gr.Textbox(label="Transcription (ಕನ್ನಡ)")
audio_output = gr.Audio(label="Reversed Audio (Optional)", type="numpy")
demo = gr.Interface(
fn=record_and_transcribe,
inputs=input_audio,
outputs=[text_output, audio_output],
description="Kannada Speech-to-Text and Reverse Audio",
)
if __name__ == "__main__":
demo.launch(share=True) |