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refactor
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SevaSk
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17
AudioRecorder.py
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17
AudioRecorder.py
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@ -0,0 +1,17 @@
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import soundcard as sc
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from Microphone import Microphone
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import pythoncom
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RECORDING_TIME = 5
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SAMPLE_RATE = 16000
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class AudioRecorder:
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def __init__(self, microphone : Microphone):
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self.microphone = microphone
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def record_into_queue(self, audio_queue, source):
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pythoncom.CoInitialize()
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with sc.get_microphone(id=self.microphone.id, include_loopback=self.microphone.loop_back).recorder(samplerate=SAMPLE_RATE) as mic:
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while True:
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data = mic.record(numframes=SAMPLE_RATE*RECORDING_TIME) # data is a frames x channels Numpy array.
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audio_queue.put((source, data))
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@ -1,82 +0,0 @@
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import pyaudio
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import queue
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def get_device_list():
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devices = []
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p = pyaudio.PyAudio()
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info = p.get_host_api_info_by_index(0)
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numdevices = info.get('deviceCount')
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for i in range(0, numdevices):
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if (p.get_device_info_by_host_api_device_index(0, i).get('maxInputChannels')) > 0:
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devices.append(p.get_device_info_by_host_api_device_index(0, i).get('name'))
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if (p.get_device_info_by_host_api_device_index(0, i).get('maxOutputChannels')) > 0:
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devices.append(p.get_device_info_by_host_api_device_index(0, i).get('name'))
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return devices
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class AudioStream(object):
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"""Opens a recording stream as a generator yielding the audio chunks."""
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def __init__(self, rate, input_device_index):
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self._rate = rate
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self._chunk = int(rate / 10) # 100ms for 16000Hz
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self.input_device_index = input_device_index
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# Create a thread-safe buffer of audio data
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self._buff = queue.Queue()
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self.closed = True
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def __enter__(self):
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self._audio_interface = pyaudio.PyAudio()
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self._audio_stream = self._audio_interface.open(
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format=pyaudio.paInt16,
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# The API currently only supports 1-channel (mono) audio
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# https://goo.gl/z757pE
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channels=1,
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rate=self._rate,
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input=True,
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frames_per_buffer=self._chunk,
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# Run the audio stream asynchronously to fill the buffer object.
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# This is necessary so that the input device's buffer doesn't
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# overflow while the calling thread makes network requests, etc.
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stream_callback=self._fill_buffer,
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input_device_index=self.input_device_index,
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)
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self.closed = False
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return self
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def __exit__(self, type, value, traceback):
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self._audio_stream.stop_stream()
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self._audio_stream.close()
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self.closed = True
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# Signal the generator to terminate so that the client's
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# streaming_recognize method will not block the process termination.
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self._buff.put(None)
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self._audio_interface.terminate()
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def _fill_buffer(self, in_data, frame_count, time_info, status_flags):
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"""Continuously collect data from the audio stream, into the buffer."""
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self._buff.put(in_data)
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return None, pyaudio.paContinue
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def generator(self):
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while not self.closed:
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# Use a blocking get() to ensure there's at least one chunk of
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# data, and stop iteration if the chunk is None, indicating the
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# end of the audio stream.
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chunk = self._buff.get()
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if chunk is None:
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return
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data = [chunk]
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# Now consume whatever other data's still buffered.
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while True:
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try:
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chunk = self._buff.get(block=False)
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if chunk is None:
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return
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data.append(chunk)
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except queue.Empty:
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break
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yield b"".join(data)
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@ -1,68 +1,36 @@
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import numpy as np
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import soundcard as sc
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import threading
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import time
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import queue
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import whisper
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import torch
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import argparse
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import wave
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import os
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from Microphone import Microphone
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TRANSCRIPT_LIMIT = 10
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RECORDING_TIME = 5
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class AudioTranscriber:
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def __init__(self, lang: str, microphone : Microphone):
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self.audio_np_array_queue = queue.Queue()
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self.transcript_data = []
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self.microphone = microphone
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self.lang = lang
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self.lock = threading.Lock()
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", default="tiny", help="Model to use",
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choices=["tiny", "base", "small", "medium", "large"])
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parser.add_argument("--non_english", action='store_true',
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help="Don't use the english model.")
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parser.add_argument("--energy_threshold", default=1000,
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help="Energy level for mic to detect.", type=int)
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parser.add_argument("--record_timeout", default=2,
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help="How real time the recording is in seconds.", type=float)
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parser.add_argument("--phrase_timeout", default=3,
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help="How much empty space between recordings before we "
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"consider it a new line in the transcription.", type=float)
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args = parser.parse_args()
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# Load / Download model
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model = args.model
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if args.model != "large" and not args.non_english:
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model = model + ".en"
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def __init__(self):
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self.transcript = []
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self.audio_model = whisper.load_model(os.getcwd() + r'\tiny.en' + '.pt')
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def transcribe(self, audio_data):
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with wave.open(f'temp_{id(self)}.wav', 'wb') as wav_file:
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wav_file.setnchannels(audio_data.shape[1])
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wav_file.setsampwidth(2)
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wav_file.setframerate(16000)
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audio_data = (audio_data * (2**15 - 1)).astype(np.int16)
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wav_file.writeframes(audio_data.tobytes())
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result = self.audio_model.transcribe(f'temp_{id(self)}.wav', fp16=torch.cuda.is_available())
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print(torch.cuda.is_available())
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text = result['text'].strip()
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return text
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def create_transcription_from_queue(self, audio_queue):
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while True:
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top_of_queue = audio_queue.get()
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source = top_of_queue[0]
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audio_data = top_of_queue[1]
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audio_data_transcription = self.transcribe(audio_data)
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# whisper gives "you" on many null inputs
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if audio_data_transcription != '' and audio_data_transcription.lower() != 'you':
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self.transcript = [source + ": [" + audio_data_transcription + ']\n\n'] + self.transcript
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def get_transcript(self):
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return self.transcript_data
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def record_into_queue(self):
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SAMPLE_RATE = 16000
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with sc.get_microphone(id=self.microphone.id, include_loopback=self.microphone.loop_back).recorder(samplerate=SAMPLE_RATE) as mic:
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while True:
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data = mic.record(numframes=SAMPLE_RATE*RECORDING_TIME) # data is a frames x channels Numpy array.
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self.audio_np_array_queue.put(data)
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return
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def transcribe_from_queue(self):
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with self.lock:
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while True:
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audio_data = self.audio_np_array_queue.get()
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with wave.open(f'temp_{self.microphone.id}.wav', 'wb') as wav_file:
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wav_file.setnchannels(audio_data.shape[1])
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wav_file.setsampwidth(2)
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wav_file.setframerate(16000)
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audio_data = (audio_data * (2**15 - 1)).astype(np.int16)
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wav_file.writeframes(audio_data.tobytes())
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result = self.audio_model.transcribe(f'temp_{self.microphone.id}.wav', fp16=torch.cuda.is_available())
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text = result['text'].strip()
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if text != '' and text.lower() != 'you': # whisper gives "you" on many null inputs
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timestamp = int(time.time())
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self.transcript_data.append({'utterance': text, 'timestamp': timestamp})
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return "".join(self.transcript)
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@ -1,17 +1,15 @@
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import openai
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from keys import OPENAI_API_KEY
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from prompts import create_prompt, INITIAL_RESPONSE
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import time
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openai.api_key = OPENAI_API_KEY
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class GPTResponder:
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def __init__(self):
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self.last_transcript = ""
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self.last_response = INITIAL_RESPONSE
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def generate_response_from_transcript(self, transcript):
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if transcript == self.last_transcript:
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return self.last_response
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo-0301",
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messages=[{"role": "system", "content": create_prompt(transcript)}],
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@ -19,9 +17,6 @@ class GPTResponder:
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)
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full_response = response.choices[0].message.content
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try:
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conversational_response = full_response.split('[')[1].split(']')[0]
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self.last_response = full_response.split('[')[1].split(']')[0]
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except:
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return self.last_response
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self.last_transcript = transcript
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self.last_response = conversational_response
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return conversational_response
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pass
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67
main.py
67
main.py
@ -1,38 +1,24 @@
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# pyinstaller --onedir --add-data "C:/Users/mcfar/AppData/Local/Programs/Python/Python310/Lib/site-packages/customtkinter;customtkinter/" --noconfirm --windowed --noconsole main.py
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import soundcard as sc
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import threading
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from AudioTranscriber import AudioTranscriber, TRANSCRIPT_LIMIT
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from gpt_responder import GPTResponder
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from AudioTranscriber import AudioTranscriber
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from GPTResponder import GPTResponder
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import customtkinter as ctk
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from Microphone import Microphone
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import soundcard as sc
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from AudioRecorder import AudioRecorder
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import queue
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def write_in_textbox(textbox, text):
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textbox.delete("0.0", "end")
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textbox.insert("0.0", text)
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#TODO make fast leetcode :)
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def create_transcript_string(transcriber_mic, transcriber_speaker, reverse = True):
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transcript_string = ""
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mic_transcript = transcriber_mic.get_transcript()
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speaker_transcript = transcriber_speaker.get_transcript()
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total_transcript = [('You', data) for data in mic_transcript] + [('Speaker', data) for data in speaker_transcript]
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sorted_transcript = sorted(total_transcript, key = lambda x: x[1]['timestamp'], reverse = reverse)
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for source, line in sorted_transcript[:TRANSCRIPT_LIMIT]:
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transcript_string += source + ": [" + line['utterance'] + ']\n\n'
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return transcript_string
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def update_transcript_UI(transcriber_mic, transcriber_thread_mic, transcriber_speaker, transcriber_thread_speaker, textbox):
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transcript_string = create_transcript_string(transcriber_mic, transcriber_speaker, reverse=True)
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def update_transcript_UI(transcriber, textbox):
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transcript_string = transcriber.get_transcript()
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textbox.delete("0.0", "end")
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textbox.insert("0.0", transcript_string)
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textbox.after(200, update_transcript_UI, transcriber_mic, transcriber_thread_mic, transcriber_speaker, transcriber_thread_speaker, textbox)
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textbox.after(300, update_transcript_UI, transcriber, textbox)
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def update_response_UI(transcriber_mic, transcriber_speaker, responder, textbox, update_interval_slider_label, update_interval_slider):
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transcript_string = create_transcript_string(transcriber_mic, transcriber_speaker,reverse=False)
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t = threading.Thread(target=lambda: responder.generate_response_from_transcript(transcript_string))
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t.start()
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#transcript_string = create_transcript_string(transcriber_mic, transcriber_speaker,reverse=False)
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textbox.configure(state="normal")
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textbox.delete("0.0", "end")
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textbox.insert("0.0", responder.last_response)
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@ -61,7 +47,7 @@ if __name__ == "__main__":
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response_textbox.grid(row=0, column=1, padx=10, pady=20, sticky="nsew")
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# Add the clear transcript button to the UI
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clear_transcript_button = ctk.CTkButton(root, text="Clear Transcript", command=lambda: clear_transcript_data(transcriber_mic, transcriber_speaker))
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clear_transcript_button = ctk.CTkButton(root, text="Clear Transcript", command=lambda: clear_transcript_data(user_transcriber, transcriber_speaker))
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clear_transcript_button.grid(row=1, column=0, padx=10, pady=3, sticky="nsew")
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# empty label, necessary for proper grid spacing
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update_interval_slider_label = ctk.CTkLabel(root, text=f"", font=("Arial", 12), text_color="#FFFCF2")
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@ -74,21 +60,23 @@ if __name__ == "__main__":
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update_interval_slider_label = ctk.CTkLabel(root, text=f"Update interval: {update_interval_slider.get()} seconds", font=("Arial", 12), text_color="#FFFCF2")
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update_interval_slider_label.grid(row=2, column=1, padx=10, pady=10, sticky="nsew")
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responder = GPTResponder()
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audio_queue = queue.Queue()
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user_mirophone = Microphone(str(sc.default_microphone().name), False)
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transcriber_mic = AudioTranscriber(lang='en-US', microphone=user_mirophone)
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recorder_thread_mic = threading.Thread(target=transcriber_mic.record_into_queue)
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transcriber_thread_mic = threading.Thread(target=transcriber_mic.transcribe_from_queue)
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recorder_thread_mic.start()
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transcriber_thread_mic.start()
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user_audio_recorder = AudioRecorder(user_mirophone)
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record_user = threading.Thread(target=user_audio_recorder.record_into_queue, args=(audio_queue, "You",))
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record_user.start()
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speaker_mirophone = Microphone(str(sc.default_speaker().name), True)
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transcriber_speaker = AudioTranscriber(lang='en-US', microphone=speaker_mirophone)
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recorder_thread_speaker = threading.Thread(target=transcriber_speaker.record_into_queue)
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transcriber_thread_speaker = threading.Thread(target=transcriber_speaker.transcribe_from_queue)
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recorder_thread_speaker.start()
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transcriber_thread_speaker.start()
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speaker_audio_recorder = AudioRecorder(speaker_mirophone)
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record_speaker = threading.Thread(target=speaker_audio_recorder.record_into_queue, args=(audio_queue, "Speaker",))
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record_speaker.start()
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global_transcriber = AudioTranscriber()
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transcribe = threading.Thread(target=global_transcriber.create_transcription_from_queue, args=(audio_queue,))
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transcribe.start()
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root.grid_rowconfigure(0, weight=100)
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root.grid_rowconfigure(1, weight=10)
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@ -98,10 +86,7 @@ if __name__ == "__main__":
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root.grid_columnconfigure(0, weight=2)
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root.grid_columnconfigure(1, weight=1)
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update_transcript_UI(transcriber_mic, transcriber_thread_mic, transcriber_speaker, transcriber_thread_speaker, transcript_textbox)
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update_response_UI(transcriber_mic, transcriber_speaker, responder, response_textbox, update_interval_slider_label, update_interval_slider)
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update_transcript_UI(global_transcriber, transcript_textbox)
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#update_response_UI(user_transcriber, transcriber_speaker, responder, response_textbox, update_interval_slider_label, update_interval_slider)
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root.mainloop()
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transcriber_thread_mic.join()
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transcriber_thread_speaker.join()
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root.mainloop()
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