ecoute/AudioTranscriber.py
2023-05-13 10:46:05 -04:00

109 lines
4.3 KiB
Python

import whisper
import torch
import wave
import os
import threading
from tempfile import NamedTemporaryFile
import custom_speech_recognition as sr
import io
from datetime import timedelta
import pyaudiowpatch as pyaudio
from AudioRecorder import DefaultMicRecorder, DefaultSpeakerRecorder
from heapq import merge
PHRASE_TIMEOUT = 3.01
MAX_PHRASES = 10
class AudioTranscriber:
def __init__(self, mic_source, speaker_source):
self.transcript_data = {"You": [], "Speaker": []}
self.transcript_changed_event = threading.Event()
self.audio_model = whisper.load_model(os.path.join(os.getcwd(), 'tiny.en.pt'))
self.audio_sources = {
"You": {
"sample_rate": mic_source.SAMPLE_RATE,
"sample_width": mic_source.SAMPLE_WIDTH,
"channels": mic_source.channels,
"last_sample": bytes(),
"last_spoken": None,
"new_phrase": True,
"process_data_func": self.process_mic_data
},
"Speaker": {
"sample_rate": speaker_source.SAMPLE_RATE,
"sample_width": speaker_source.SAMPLE_WIDTH,
"channels": speaker_source.channels,
"last_sample": bytes(),
"last_spoken": None,
"new_phrase": True,
"process_data_func": self.process_speaker_data
}
}
def transcribe_audio_queue(self, audio_queue):
while True:
who_spoke, data, time_spoken = audio_queue.get()
self.update_last_sample_and_phrase_status(who_spoke, data, time_spoken)
source_info = self.audio_sources[who_spoke]
temp_file = source_info["process_data_func"](source_info["last_sample"])
text = self.get_transcription(temp_file)
if text != '' and text.lower() != 'you':
self.update_transcript(who_spoke, text, time_spoken)
self.transcript_changed_event.set()
def update_last_sample_and_phrase_status(self, who_spoke, data, time_spoken):
source_info = self.audio_sources[who_spoke]
if source_info["last_spoken"] and time_spoken - source_info["last_spoken"] > timedelta(seconds=PHRASE_TIMEOUT):
source_info["last_sample"] = bytes()
source_info["new_phrase"] = True
else:
source_info["new_phrase"] = False
source_info["last_sample"] += data
source_info["last_spoken"] = time_spoken
def process_mic_data(self, data):
temp_file = NamedTemporaryFile().name
audio_data = sr.AudioData(data, self.audio_sources["You"]["sample_rate"], self.audio_sources["You"]["sample_width"])
wav_data = io.BytesIO(audio_data.get_wav_data())
with open(temp_file, 'w+b') as f:
f.write(wav_data.read())
return temp_file
def process_speaker_data(self, data):
temp_file = NamedTemporaryFile().name
with wave.open(temp_file, 'wb') as wf:
wf.setnchannels(self.audio_sources["Speaker"]["channels"])
p = pyaudio.PyAudio()
wf.setsampwidth(p.get_sample_size(pyaudio.paInt16))
wf.setframerate(self.audio_sources["Speaker"]["sample_rate"])
wf.writeframes(data)
return temp_file
def get_transcription(self, file_path):
result = self.audio_model.transcribe(file_path, fp16=torch.cuda.is_available())
return result['text'].strip()
def update_transcript(self, who_spoke, text, time_spoken):
source_info = self.audio_sources[who_spoke]
transcript = self.transcript_data[who_spoke]
if source_info["new_phrase"] or len(transcript) == 0:
if len(transcript) > MAX_PHRASES:
transcript.pop(0) # remove oldest phrase
transcript.insert(0, (f"{who_spoke}: [{text}]\n\n", time_spoken))
else:
transcript[0] = (f"{who_spoke}: [{text}]\n\n", time_spoken)
def get_transcript(self):
combined_transcript = list(merge(
self.transcript_data["You"], self.transcript_data["Speaker"],
key=lambda x: x[1], reverse=True))
combined_transcript = combined_transcript[:MAX_PHRASES]
return "".join([t[0] for t in combined_transcript])
def clear_transcript_data(self):
self.transcript_data["You"].clear()
self.transcript_data["Speaker"].clear()