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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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