98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
from io import BytesIO
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from threading import Event
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import wave
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from speech_recognition import Recognizer, AudioData, AudioFile
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from datetime import timedelta
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from pyaudiowpatch import get_sample_size, paInt16
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from .transcription_languages import transcription_lang
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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, phrase_timeout, max_phrases):
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self.speaker = speaker
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self.phrase_timeout = phrase_timeout
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self.max_phrases = max_phrases
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self.transcript_data = []
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self.transcript_changed_event = Event()
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self.audio_recognizer = 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.processSpeakerData if speaker else self.processSpeakerData
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}
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def transcribeAudioQueue(self, audio_queue, language):
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# while True:
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audio, time_spoken = audio_queue.get()
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self.updateLastSampleAndPhraseStatus(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=transcription_lang[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.updateTranscript(text)
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def updateLastSampleAndPhraseStatus(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=self.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 processMicData(self):
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audio_data = 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 processSpeakerData(self):
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temp_file = 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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wf.setsampwidth(get_sample_size(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 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 updateTranscript(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) > self.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 getTranscript(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 clearTranscriptData(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 |