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Create utils.py
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utils.py
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import numpy as np
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import subprocess
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import soundfile as sf
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from speech_recognition import AudioFile, Recognizer
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greeting_list = ["γγγ£γγγγΎγ",
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"γγγ£γγγ",
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"γγγ£γγγγΎγγΌ",
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"γγγ«γ‘γ―",
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"γγ―γγγγγγΎγ",
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"γγ―γγ",
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"γγ―γγΌ",
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"γγ―γΌ",
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]
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def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array:
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"""
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Helper function to read an audio file through ffmpeg.
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"""
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ar = f"{sampling_rate}"
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ac = "1"
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format_for_conversion = "f32le"
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ffmpeg_command = [
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"ffmpeg",
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"-i",
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"pipe:0",
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"-ac",
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ac,
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"-ar",
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ar,
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"-f",
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format_for_conversion,
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"-hide_banner",
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"-loglevel",
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"quiet",
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"pipe:1",
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]
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try:
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ffmpeg_process = subprocess.Popen(ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
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except FileNotFoundError:
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raise ValueError("ffmpeg was not found but is required to load audio files from filename")
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output_stream = ffmpeg_process.communicate(bpayload)
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out_bytes = output_stream[0]
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audio = np.frombuffer(out_bytes, np.float32)
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sf.write('temp.wav', audio, sampling_rate, subtype='PCM_16')
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return 'temp.wav'
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def stt(audio: object, language='ja') -> str:
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"""Converts speech to text.
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Args:
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audio: record of user speech
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language (str): language of text
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Returns:
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text (str): recognized speech of user
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"""
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# Create a Recognizer object
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r = Recognizer()
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# Open the audio file
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with AudioFile(audio) as source:
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# Listen for the data (load audio to memory)
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audio_data = r.record(source)
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# Transcribe the audio using Google's speech-to-text API
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text = r.recognize_google(audio_data, language=language)
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return text
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