Add script to convert pdf to audiobook

pdf-to-audiobook/Dockerfile

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+# Dockerfile
+FROM python:3.10-slim
+
+# 1) System deps for audio processing
+RUN apt-get update \
+ && apt-get install -y --no-install-recommends \
+    build-essential \
+    ffmpeg \
+    libsndfile1 \
+ && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# 2) Python deps
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# 3) Copy your script + code
+COPY . .
+
+# 4) Default entrypoint is just the script; pass args via docker-compose or CLI
+ENTRYPOINT ["python", "pdf_to_audiobook.py"]
+

pdf-to-audiobook/pdf_to_audiobook.py

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+
+
+#!/usr/bin/env python3
+"""
+pdf_to_audiobook.py
+
+1) Always convert PDF → Markdown via marker + your OpenAI-compatible endpoint.
+2) If you pass --voice-name, use Coqui XTTS (voice cloning);
+   otherwise use Kokoro TTS with the “Daniel” speaker.
+3) Manage your 6-sec snippets in voices/ directory.
+"""
+
+import os
+import argparse
+import tempfile
+import numpy as np
+import torch
+import soundfile as sf
+from pathlib import Path
+from pdfminer.high_level import extract_text
+from pydub import AudioSegment# Allow Coqui’s XttsConfig class to be unpickled
+
+# ─── Whitelist Coqui classes for safe CPU‐only loading ────────────────────────
+from TTS.tts.configs.xtts_config import XttsConfig
+from TTS.tts.models.xtts import XttsAudioConfig
+from TTS.config.shared_configs import BaseDatasetConfig
+from TTS.tts.models.xtts import XttsArgs
+from torch.serialization import add_safe_globals
+add_safe_globals([XttsConfig, XttsAudioConfig, BaseDatasetConfig, XttsArgs])
+
+# ─── Coqui XTTS API ─────────────────────────────────────────────────────────
+from TTS.api import TTS
+
+# ─── Kokoro TTS API ─────────────────────────────────────────────────────────
+from kokoro import KPipeline
+
+# ─── marker PDF→Markdown API ────────────────────────────────────────────────
+from marker.converters.pdf import PdfConverter
+from marker.models           import create_model_dict
+from marker.config.parser    import ConfigParser
+from marker.output           import text_from_rendered
+
+VOICE_DIR = Path("voices")
+
+def list_available_voices():
+    return sorted(p.stem for p in VOICE_DIR.glob("*.wav"))
+
+def convert_pdf_to_markdown(pdf_path: str) -> str:
+    key = os.getenv("OPENAI_API_KEY")
+    model = os.getenv("OPENAI_MODEL")
+    url = os.getenv("OPENAI_BASE_URL")
+    if not key:
+        raise RuntimeError("Please set OPENAI_API_KEY in your environment")
+    cfg = {
+        "output_format":   "markdown",
+        "use_llm":         True,
+        "llm_service":     "marker.services.openai.OpenAIService",
+        "openai_api_key":  key,
+        "openai_model":    model,
+        "openai_base_url": url,
+    }
+    parser    = ConfigParser(cfg)
+    converter = PdfConverter(
+        config         = parser.generate_config_dict(),
+        artifact_dict  = create_model_dict(),
+        processor_list = parser.get_processors(),
+        renderer       = parser.get_renderer(),
+        llm_service    = parser.get_llm_service(),
+    )
+    rendered = converter(pdf_path)
+    markdown, _, _ = text_from_rendered(rendered)
+    return markdown
+
+def synthesize_with_coqui(
+    text: str,
+    out_mp3: str,
+    voice: str,
+    speaker: str | None,
+    lang:    str,
+):
+    use_gpu = torch.cuda.is_available()
+    tts     = TTS(model_name="tts_models/multilingual/xtts_v2", gpu=use_gpu)
+
+    spk_wav  = [voice]
+    spk_name = speaker or tts.speakers[0]
+    print(f"⚙️ Using Coqui voice sample “{voice}” → speaker “{spk_name}”")
+
+    tmp_wav = tempfile.mktemp(suffix=".wav")
+    tts.tts_to_file(
+        text            = text,
+        file_path       = tmp_wav,
+        speaker_wav     = spk_wav,
+        speaker         = spk_name,
+        language        = lang,
+        split_sentences = True,
+    )
+    AudioSegment.from_wav(tmp_wav).export(out_mp3, format="mp3")
+    print(f"✅ Coqui MP3 saved → {out_mp3}")
+
+def synthesize_with_kokoro(
+    text:   str,
+    out_mp3:str,
+    lang:   str,
+):
+    pipeline = KPipeline(lang_code=lang)
+    chunks = []
+    # Daniel is the chosen voice
+    for _, _, audio in pipeline(text, voice="Daniel"):
+        chunks.append(audio)
+    full = np.concatenate(chunks, axis=0)
+
+    tmp_wav = tempfile.mktemp(suffix=".wav")
+    sf.write(tmp_wav, full, 24000)
+    AudioSegment.from_wav(tmp_wav).export(out_mp3, format="mp3")
+    print(f"✅ Kokoro MP3 saved → {out_mp3}")
+
+def main():
+    # ensure voices/ exists
+    VOICE_DIR.mkdir(exist_ok=True)
+
+    ap = argparse.ArgumentParser(
+        description="PDF→Markdown→Audiobook (Coqui or Kokoro/Daniel)"
+    )
+    ap.add_argument("pdf",           help="Input PDF file")
+    ap.add_argument("--list-voices", action="store_true",
+                    help="Show available voice snippets and exit")
+    ap.add_argument("--voice-name",  help="Name of a .mp3 in voices/ to clone")
+    ap.add_argument("--speaker",     help="Coqui built-in speaker (default if none)")
+    ap.add_argument("--lang",        default="en",  help="Language code")
+    ap.add_argument("--out",         default="audiobook.mp3",
+                    help="Output MP3 path")
+    args = ap.parse_args()
+
+    if args.list_voices:
+        print("Available voices:")
+        for name in list_available_voices():
+            print("  ", name)
+        return
+
+    # 1) PDF→Markdown
+    print("🔄 Converting PDF → Markdown…")
+    text = convert_pdf_to_markdown(args.pdf)
+
+    # 2) Choose engine
+    if args.voice_name:
+        wav_path = VOICE_DIR / f"{args.voice_name}.wav"
+        if not wav_path.is_file():
+            raise FileNotFoundError(f"voices/{args.voice_name}.wav not found.")
+        synthesize_with_coqui(text, args.out, str(wav_path),
+                              args.speaker, args.lang)
+    else:
+        synthesize_with_kokoro(text, args.out, args.lang)
+
+if __name__ == "__main__":
+    main()
+

pdf-to-audiobook/requirements.txt

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+marker-pdf
+kokoro>=0.9.4
+soundfile
+TTS
+pdfminer.six
+pydub
+torch
+"transformers<4.50.0"
+