app.py

import gradio as gr
from pydub import AudioSegment
from io import BytesIO
import imageio_ffmpeg
from docx import Document
from openai import OpenAI

# Setting up paths and initial configurations
ffmpeg_path = imageio_ffmpeg.get_ffmpeg_exe()
AudioSegment.converter = ffmpeg_path
AudioSegment.ffprobe = ffmpeg_path
client = OpenAI()

def is_valid_mp3(data):
    headers = data.read(10)
    data.seek(0)
    return headers.startswith(b'ID3') or headers[0:2] == b'\xff\xfb'

def process_audio(audio_file_path, output_format, progress=None):
    if not audio_file_path:
        return "No audio file provided.", None

    file_type = audio_file_path.split('.')[-1]
    audio = AudioSegment.from_file(audio_file_path, format=file_type)
    if file_type != "mp3":
        audio_data = BytesIO()
        audio.export(audio_data, format="mp3")
        audio_data.seek(0)
        audio = AudioSegment.from_file(audio_data, format="mp3")

    duration_ms = len(audio)
    max_size_bytes = 26_214_400
    max_duration_per_part = duration_ms * max_size_bytes / len(audio.raw_data)

    parts = []
    start_ms = 0
    total_parts = int(duration_ms / max_duration_per_part) + 1
    part_index = 0

    while start_ms < duration_ms:
        end_ms = min(start_ms + max_duration_per_part, duration_ms)
        part = audio[start_ms:end_ms]
        parts.append(part)
        start_ms = end_ms
        part_index += 1
        if progress:
            progress(part_index / total_parts)

    full_transcript = ""
    for part_index, part in enumerate(parts):
        part_data = BytesIO()
        part.export(part_data, format="mp3")
        part_data.seek(0)
        if is_valid_mp3(part_data):
            part_data.name = f"part_{part_index}.mp3"
            transcript_part = client.audio.transcriptions.create(
                model="whisper-1",
                file=part_data
            )
            full_transcript += transcript_part.text + "\n"

  #  text_bytes = BytesIO(full_transcript.encode('utf-8'))
    return full_transcript #, text_bytes

iface = gr.Interface(
    fn=process_audio,
    inputs=gr.Audio(type="filepath", label="音声ファイルをアップロード"),
    outputs=[
        gr.Textbox(label="出力", show_copy_button=True),
       # gr.File(label=".txtダウンロード", type='binary')
    ],
    title="音声ファイルをテキストに変換",
    description="目安として10分の音声は1分程度かかります。",
    allow_flagging="never"
)

iface.launch()