app.py

import argparse
import io
import os
import speech_recognition as sr
import whisper
import torch

from datetime import datetime, timedelta
from queue import Queue
from tempfile import NamedTemporaryFile
from time import sleep
from sys import platform


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model",
        default="medium",
        help="Model to use",
        choices=["tiny", "base", "small", "medium", "large"],
    )
    parser.add_argument(
        "--non_english", action="store_true", help="Don't use the english model."
    )
    parser.add_argument(
        "--energy_threshold",
        default=1000,
        help="Energy level for mic to detect.",
        type=int,
    )
    parser.add_argument(
        "--record_timeout",
        default=2,
        help="How real time the recording is in seconds.",
        type=float,
    )
    parser.add_argument(
        "--phrase_timeout",
        default=3,
        help="How much empty space between recordings before we "
        "consider it a new line in the transcription.",
        type=float,
    )
    if "linux" in platform:
        parser.add_argument(
            "--default_microphone",
            default="pulse",
            help="Default microphone name for SpeechRecognition. "
            "Run this with 'list' to view available Microphones.",
            type=str,
        )
    args = parser.parse_args()

    # The last time a recording was retreived from the queue.
    phrase_time = None
    # Current raw audio bytes.
    last_sample = bytes()
    # Thread safe Queue for passing data from the threaded recording callback.
    data_queue = Queue()
    # We use SpeechRecognizer to record our audio because it has a nice feauture where it can detect when speech ends.
    recorder = sr.Recognizer()
    recorder.energy_threshold = args.energy_threshold
    # Definitely do this, dynamic energy compensation lowers the energy threshold dramtically to a point where the SpeechRecognizer never stops recording.
    recorder.dynamic_energy_threshold = False

    # Important for linux users.
    # Prevents permanent application hang and crash by using the wrong Microphone
    if "linux" in platform:
        mic_name = args.default_microphone
        if not mic_name or mic_name == "list":
            print("Available microphone devices are: ")
            for index, name in enumerate(sr.Microphone.list_microphone_names()):
                print(f'Microphone with name "{name}" found')
            return
        else:
            for index, name in enumerate(sr.Microphone.list_microphone_names()):
                if mic_name in name:
                    source = sr.Microphone(sample_rate=16000, device_index=index)
                    break
    else:
        source = sr.Microphone(sample_rate=16000)

    # Load / Download model
    model = args.model
    if args.model != "large" and not args.non_english:
        model = model + ".en"
    audio_model = whisper.load_model(model)

    record_timeout = args.record_timeout
    phrase_timeout = args.phrase_timeout

    temp_file = NamedTemporaryFile().name
    transcription = [""]

    with source:
        recorder.adjust_for_ambient_noise(source)

    def record_callback(_, audio: sr.AudioData) -> None:
        """
        Threaded callback function to recieve audio data when recordings finish.
        audio: An AudioData containing the recorded bytes.
        """
        # Grab the raw bytes and push it into the thread safe queue.
        data = audio.get_raw_data()
        data_queue.put(data)

    # Create a background thread that will pass us raw audio bytes.
    # We could do this manually but SpeechRecognizer provides a nice helper.
    recorder.listen_in_background(
        source, record_callback, phrase_time_limit=record_timeout
    )

    # Cue the user that we're ready to go.
    print("Model loaded.\n")

    while True:
        try:
            now = datetime.utcnow()
            # Pull raw recorded audio from the queue.
            if not data_queue.empty():
                phrase_complete = False
                # If enough time has passed between recordings, consider the phrase complete.
                # Clear the current working audio buffer to start over with the new data.
                if phrase_time and now - phrase_time > timedelta(
                    seconds=phrase_timeout
                ):
                    last_sample = bytes()
                    phrase_complete = True
                # This is the last time we received new audio data from the queue.
                phrase_time = now

                # Concatenate our current audio data with the latest audio data.
                while not data_queue.empty():
                    data = data_queue.get()
                    last_sample += data

                # Use AudioData to convert the raw data to wav data.
                audio_data = sr.AudioData(
                    last_sample, source.SAMPLE_RATE, source.SAMPLE_WIDTH
                )
                wav_data = io.BytesIO(audio_data.get_wav_data())

                # Write wav data to the temporary file as bytes.
                with open(temp_file, "w+b") as f:
                    f.write(wav_data.read())

                # Read the transcription.
                result = audio_model.transcribe(
                    temp_file, fp16=torch.cuda.is_available()
                )
                text = result["text"].strip()

                # If we detected a pause between recordings, add a new item to our transcripion.
                # Otherwise edit the existing one.
                if phrase_complete:
                    transcription.append(text)
                else:
                    transcription[-1] = text

                # Clear the console to reprint the updated transcription.
                os.system("cls" if os.name == "nt" else "clear")
                for line in transcription:
                    print(line)
                # Flush stdout.
                print("", end="", flush=True)

                # Infinite loops are bad for processors, must sleep.
                sleep(0.25)
        except KeyboardInterrupt:
            break

    print("\n\nTranscription:")
    for line in transcription:
        print(line)


if __name__ == "__main__":
    main()