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111 lines
3.4 KiB
Python
111 lines
3.4 KiB
Python
from dotenv import load_dotenv
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from pydub import AudioSegment
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from openai import OpenAI
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import os
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import argparse
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class Whisper:
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def __init__(self):
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env_file = os.path.expanduser("~/.config/fabric/.env")
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load_dotenv(env_file)
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try:
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apikey = os.environ["OPENAI_API_KEY"]
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self.client = OpenAI()
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self.client.api_key = apikey
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except KeyError:
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print("OPENAI_API_KEY not found in environment variables.")
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except FileNotFoundError:
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print("No API key found. Use the --apikey option to set the key")
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self.whole_response = []
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def split_audio(self, file_path):
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"""
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Splits the audio file into segments of the given length.
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Args:
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- file_path: The path to the audio file.
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- segment_length_ms: Length of each segment in milliseconds.
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Returns:
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- A list of audio segments.
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"""
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audio = AudioSegment.from_file(file_path)
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segments = []
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segment_length_ms = 10 * 60 * 1000 # 10 minutes in milliseconds
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for start_ms in range(0, len(audio), segment_length_ms):
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end_ms = start_ms + segment_length_ms
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segment = audio[start_ms:end_ms]
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segments.append(segment)
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return segments
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def process_segment(self, segment):
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""" Transcribe an audio file and print the transcript.
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Args:
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audio_file (str): The path to the audio file to be transcribed.
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Returns:
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None
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"""
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try:
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# if audio_file.startswith("http"):
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# response = requests.get(audio_file)
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# response.raise_for_status()
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# with tempfile.NamedTemporaryFile(delete=False) as f:
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# f.write(response.content)
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# audio_file = f.name
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audio_file = open(segment, "rb")
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response = self.client.audio.transcriptions.create(
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model="whisper-1",
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file=audio_file
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)
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self.whole_response.append(response.text)
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except Exception as e:
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print(f"Error: {e}")
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def process_file(self, audio_file):
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""" Transcribe an audio file and print the transcript.
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Args:
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audio_file (str): The path to the audio file to be transcribed.
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Returns:
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None
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"""
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try:
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# if audio_file.startswith("http"):
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# response = requests.get(audio_file)
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# response.raise_for_status()
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# with tempfile.NamedTemporaryFile(delete=False) as f:
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# f.write(response.content)
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# audio_file = f.name
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segments = self.split_audio(audio_file)
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for i, segment in enumerate(segments):
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segment_file_path = f"segment_{i}.mp3"
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segment.export(segment_file_path, format="mp3")
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self.process_segment(segment_file_path)
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print(' '.join(self.whole_response))
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except Exception as e:
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print(f"Error: {e}")
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def main():
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parser = argparse.ArgumentParser(description="Transcribe an audio file.")
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parser.add_argument(
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"audio_file", help="The path to the audio file to be transcribed.")
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args = parser.parse_args()
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whisper = Whisper()
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whisper.process_file(args.audio_file)
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if __name__ == "__main__":
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main()
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