translation_demo.py

import argparse
import io
import os
import speech_recognition as sr
import whisper
import torch
import subprocess
import nltk
from nltk.tokenize import sent_tokenize




from datetime import datetime, timedelta
from queue import Queue
from tempfile import NamedTemporaryFile
from time import sleep
from sys import platform
from faster_whisper import WhisperModel
from translatepy.translators.google import GoogleTranslate
from TranscriptionWindow import TranscriptionWindow
    
def main():
    
   
    parser = argparse.ArgumentParser()
    parser.add_argument("--model", default="medium", help="Model to use",
                        choices=["tiny", "base", "small", "medium", "large"])
    parser.add_argument("--device", default="auto", help="device to user for CTranslate2 inference",
                        choices=["auto", "cuda","cpu"])                   
    parser.add_argument("--compute_type", default="auto", help="Type of quantization to use",
                        choices=["auto", "int8", "int8_floatt16", "float16", "int16", "float32"])
    parser.add_argument("--translation_lang", default='English',
                        help="Which language should we translate into." , type=str)
    parser.add_argument("--non_english", action='store_true',
                        help="Don't use the english model.")
    parser.add_argument("--threads", default=0,
                        help="number of threads used for CPU inference", type=int)
    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)
    
    if args.model == "large":
        args.model = "large-v2"    
    
    model = args.model
    if args.model != "large-v2" and not args.non_english:
        model = model + ".en"
        
    translation_lang = args.translation_lang    
    device = args.device
    if device == "cpu":
        compute_type = "int8"
    else:
        compute_type = args.compute_type
    cpu_threads = args.threads
    
    nltk.download('punkt')
    audio_model = WhisperModel(model, device = device, compute_type = compute_type , cpu_threads = cpu_threads)
    window = TranscriptionWindow()
    
    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.
                text = ""
                    
                segments, info = audio_model.transcribe(temp_file)
                for segment in segments:
                    text += segment.text
                #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
                last_four_elements = transcription[-10:]
                result = ''.join(last_four_elements)    
                sentences = sent_tokenize(result)
                window.update_text(sentences, translation_lang)
                # Clear the console to reprint the updated transcription.

                # 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()