97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
import io
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import threading
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import wave
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import custom_speech_recognition as sr
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from datetime import timedelta
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import pyaudiowpatch as pyaudio
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PHRASE_TIMEOUT = 3
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MAX_PHRASES = 10
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class AudioTranscriber:
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def __init__(self, speaker, source, language):
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self.speaker = speaker
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self.language = language
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self.transcript_data = []
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self.transcript_changed_event = threading.Event()
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self.audio_recognizer = sr.Recognizer()
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self.audio_sources = {
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"sample_rate": source.SAMPLE_RATE,
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"sample_width": source.SAMPLE_WIDTH,
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"channels": source.channels,
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"last_sample": bytes(),
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"last_spoken": None,
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"new_phrase": True,
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"process_data_func": self.process_speaker_data if speaker else self.process_speaker_data
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}
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def transcribe_audio_queue(self, audio_queue):
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# while True:
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audio, time_spoken = audio_queue.get()
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self.update_last_sample_and_phrase_status(audio, time_spoken)
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text = ''
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try:
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# fd, path = tempfile.mkstemp(suffix=".wav")
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# os.close(fd)
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audio_data = self.audio_sources["process_data_func"]()
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text = self.audio_recognizer.recognize_google(audio_data, language=self.language)
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except Exception as e:
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pass
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finally:
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pass
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# os.unlink(path)
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if text != '':
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self.update_transcript(text)
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def update_last_sample_and_phrase_status(self, data, time_spoken):
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source_info = self.audio_sources
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if source_info["last_spoken"] and time_spoken - source_info["last_spoken"] > timedelta(seconds=PHRASE_TIMEOUT):
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source_info["last_sample"] = bytes()
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source_info["new_phrase"] = True
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else:
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source_info["new_phrase"] = False
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source_info["last_sample"] += data
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source_info["last_spoken"] = time_spoken
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def process_mic_data(self):
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audio_data = sr.AudioData(self.audio_sources["last_sample"], self.audio_sources["sample_rate"], self.audio_sources["sample_width"])
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return audio_data
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def process_speaker_data(self):
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temp_file = io.BytesIO()
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with wave.open(temp_file, 'wb') as wf:
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wf.setnchannels(self.audio_sources["channels"])
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p = pyaudio.PyAudio()
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wf.setsampwidth(p.get_sample_size(pyaudio.paInt16))
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wf.setframerate(self.audio_sources["sample_rate"])
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wf.writeframes(self.audio_sources["last_sample"])
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temp_file.seek(0)
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with sr.AudioFile(temp_file) as source:
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audio = self.audio_recognizer.record(source)
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return audio
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def update_transcript(self, text):
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source_info = self.audio_sources
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transcript = self.transcript_data
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if source_info["new_phrase"] or len(transcript) == 0:
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if len(transcript) > MAX_PHRASES:
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transcript.pop(-1)
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transcript.insert(0, text)
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else:
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transcript[0] = text
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def get_transcript(self):
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if len(self.transcript_data) > 0:
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text = self.transcript_data.pop(-1)
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else:
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text = ""
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return text
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def clear_transcript_data(self):
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self.transcript_data.clear()
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self.audio_sources["last_sample"] = bytes()
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self.audio_sources["new_phrase"] = True |