oldapp.py

import time
import datetime
import logging
import soundfile
import streamlit as st
from streamlit_webrtc import webrtc_streamer, AudioProcessorBase, WebRtcMode
import numpy as np
import pydub
from pathlib import Path

from asr import load_model, inference

LOG_DIR = "./logs"
DATA_DIR = "./data"
logger = logging.getLogger(__name__)


# Define a custom audio processor to handle microphone input
class AudioProcessor(AudioProcessorBase):
    def __init__(self):
        self.audio_data = []

    def recv_audio(self, frame):
        # Convert the audio frame to a NumPy array
        audio_array = np.frombuffer(frame.to_ndarray(), dtype=np.int16)
        self.audio_data.append(audio_array)
        return frame

    def get_audio_data(self):
        # Combine all captured audio data
        if self.audio_data:
            combined = np.concatenate(self.audio_data, axis=0)
            return combined
        return None


def upload_audio() -> Path:
    # Upload audio file
    uploaded_file = st.file_uploader("Choose a audio file(wav, mp3, flac)", type=['wav','mp3','flac'])
    if uploaded_file is not None:
        # Save audio file
        audio_data, samplerate = soundfile.read(uploaded_file)
        
        # Make save directory
        now = datetime.datetime.now()
        now_time = now.strftime('%Y-%m-%d-%H:%M:%S')
        audio_dir = Path(DATA_DIR) / f"{now_time}"
        audio_dir.mkdir(parents=True, exist_ok=True)
        
        audio_path = audio_dir / uploaded_file.name
        soundfile.write(audio_path, audio_data, samplerate)
        
        # Show audio file
        with open(audio_path, 'rb') as audio_file:
            audio_bytes = audio_file.read()
        
        st.audio(audio_bytes, format=uploaded_file.type)
        
        return audio_path

@st.cache_resource(show_spinner=False)
def call_load_model():
    generator = load_model()
    return generator

def main():
    st.header("Speech-to-Text app with streamlit")
    st.markdown(
        """
        This STT app is using a fine-tuned MMS ASR model.
        """
    )
    
    audio_path = upload_audio()
    logger.info(f"Uploaded audio file: {audio_path}")
    
    with st.spinner(text="Wait for loading ASR Model..."):
        generator = call_load_model()
    
    if audio_path is not None:
        start_time = time.time()
        with st.spinner(text='Wait for inference...'):
            output = inference(generator, audio_path)

        end_time = time.time()

        process_time = time.gmtime(end_time - start_time)
        process_time = time.strftime("%H hour %M min %S secs", process_time)

        st.success(f"Inference finished in {process_time}.")
        st.write(f"output: {output['text']}")
    
    st.title("Microphone Input for ASR")

    # Initialize the audio processor
    audio_processor = AudioProcessor()

    webrtc_streamer(
        key="audio",
        mode=WebRtcMode.SENDONLY,
        audio_processor_factory=lambda: audio_processor,
        media_stream_constraints={"audio": True, "video": False},
    )


    if st.button("Process Audio"):
        audio_data = audio_processor.get_audio_data()
        if audio_data is not None:
            # Convert the NumPy array to a WAV-like audio segment
            audio_segment = pydub.AudioSegment(
                audio_data.tobytes(),
                frame_rate=16000,  # Default WebRTC audio frame rate
                sample_width=2,  # 16-bit audio
                channels=1  # Mono
            )
            # Save or process audio_segment as needed
            st.success("Audio captured successfully!")
            # st.audio(audio_segment.export(format="wav"), format="audio/wav")
        else:
            st.warning("No audio data captured!")


    if st.button("Transcribe Audio"):
        if audio_data is not None:
            # Perform ASR on the audio segment
            transcription = inference(generator, audio_segment.raw_data)
            st.text_area("Transcription", transcription["text"])
        else:
            st.warning("No audio data to transcribe!")


if __name__ == "__main__":
    # Setting logger
    logger.setLevel(logging.INFO)
    
    formatter = logging.Formatter("%(levelname)8s %(asctime)s %(name)s %(message)s")
    
    stream_handler = logging.StreamHandler()
    stream_handler.setFormatter(formatter)
    logger.addHandler(stream_handler)
    
    now = datetime.datetime.now()
    now_time = now.strftime('%Y-%m-%d-%H:%M:%S')
    log_dir = Path(LOG_DIR)
    log_dir.mkdir(parents=True, exist_ok=True)
    log_file = log_dir / f"{now_time}.log"
    file_handler = logging.FileHandler(str(log_file), encoding='utf-8')
    file_handler.setFormatter(formatter)
    logger.addHandler(file_handler)
    
    logger.info('Start App')
    
    main()