app.py

import torch
import torchaudio
import gradio as gr
import time
import numpy as np
import scipy.io.wavfile
from omegaconf import OmegaConf  # ✅ Fix: Import omegaconf

# ✅ 1️⃣ Load Silero STT Model for CPU
device = torch.device("cpu")  
torch_dtype = torch.float32

# ✅ 2️⃣ Load Silero Model & Decoder with `trust_repo=True`
torch.set_num_threads(4)  
model, decoder, utils = torch.hub.load(repo_or_dir="snakers4/silero-models",
                                       model="silero_stt",
                                       language="en",
                                       device=device,
                                       trust_repo=True)  # ✅ Fix: Avoids untrusted repo warning
(read_batch, split_into_batches, read_audio, prepare_model_input) = utils

# ✅ 3️⃣ Real-Time Streaming Transcription (Microphone)
def stream_transcribe(stream, new_chunk):
    start_time = time.time()
    try:
        sr, y = new_chunk

        # ✅ Convert stereo to mono
        if y.ndim > 1:
            y = y.mean(axis=1)

        y = y.astype(np.float32)
        y /= np.max(np.abs(y))

        # ✅ Resample audio to 16kHz using torchaudio
        y_tensor = torch.tensor(y)
        y_resampled = torchaudio.functional.resample(y_tensor, orig_freq=sr, new_freq=16000).numpy()

        # ✅ Append to Stream
        if stream is not None:
            stream = np.concatenate([stream, y_resampled])
        else:
            stream = y_resampled

        # ✅ Prepare Model Input
        input_tensor = torch.from_numpy(stream).unsqueeze(0)
        input_tensor = prepare_model_input(input_tensor, device=device)

        # ✅ Run Transcription
        transcription = model(input_tensor)
        text = decoder(transcription[0].cpu())

        latency = time.time() - start_time
        return stream, text, f"{latency:.2f} sec"

    except Exception as e:
        print(f"Error: {e}")
        return stream, str(e), "Error"

# ✅ 4️⃣ Transcription for File Upload
def transcribe(inputs, previous_transcription):
    start_time = time.time()
    try:
        # ✅ Convert file input to correct format
        sample_rate, audio_data = inputs

        # ✅ Resample using torchaudio (optimized)
        audio_tensor = torch.tensor(audio_data)
        resampled_audio = torchaudio.functional.resample(audio_tensor, orig_freq=sample_rate, new_freq=16000).numpy()

        # ✅ Prepare Model Input
        input_tensor = torch.from_numpy(resampled_audio).unsqueeze(0)
        input_tensor = prepare_model_input(input_tensor, device=device)

        # ✅ Run Transcription
        transcription = model(input_tensor)
        text = decoder(transcription[0].cpu())

        previous_transcription += text
        latency = time.time() - start_time

        return previous_transcription, f"{latency:.2f} sec"

    except Exception as e:
        print(f"Error: {e}")
        return previous_transcription, "Error"

# ✅ 5️⃣ Clear Function
def clear():
    return ""

# ✅ 6️⃣ Gradio Interface (Microphone Streaming)
with gr.Blocks() as microphone:
    gr.Markdown(f"# Silero STT - Real-Time Transcription (Optimized CPU) 🎙️")
    gr.Markdown("Using `Silero STT` for lightweight, accurate speech-to-text.")

    with gr.Row():
        input_audio_microphone = gr.Audio(sources=["microphone"], type="numpy", streaming=True)
        output = gr.Textbox(label="Live Transcription", value="")
        latency_textbox = gr.Textbox(label="Latency (seconds)", value="0.0")

    with gr.Row():
        clear_button = gr.Button("Clear Output")

    state = gr.State()
    input_audio_microphone.stream(
        stream_transcribe, [state, input_audio_microphone], 
        [state, output, latency_textbox], time_limit=30, stream_every=1
    )
    clear_button.click(clear, outputs=[output])

# ✅ 7️⃣ Gradio Interface (File Upload)
with gr.Blocks() as file:
    gr.Markdown(f"# Upload Audio File for Transcription 🎵")
    gr.Markdown("Using `Silero STT` for offline, high-accuracy transcription.")

    with gr.Row():
        input_audio = gr.Audio(sources=["upload"], type="numpy")
        output = gr.Textbox(label="Transcription", value="")
        latency_textbox = gr.Textbox(label="Latency (seconds)", value="0.0")

    with gr.Row():
        submit_button = gr.Button("Submit")
        clear_button = gr.Button("Clear Output")

    submit_button.click(transcribe, [input_audio, output], [output, latency_textbox])
    clear_button.click(clear, outputs=[output])

# ✅ 8️⃣ Final Gradio App
with gr.Blocks(theme=gr.themes.Ocean()) as demo:
    gr.TabbedInterface([microphone, file], ["Microphone", "Upload Audio"])

# ✅ 9️⃣ Run Gradio Locally
if __name__ == "__main__":
    demo.launch()