Automatic Podcast Transcription Implementation

Implementing Automatic Podcast Transcription. Podcast transcription opens up content for SEO, makes it accessible to the hearing-impaired, and allows for the creation of articles and summaries from audio content. Key requirements: good quality for conversational speech of multiple speakers, support for long recordings (1–3 hours). ### Optimal stack: Whisper large-v3 + Pyannote diarization — the best open-source choice for podcasts. AssemblyAI — the best cloud-based option with ready-made diarization and chapter detection. ### Quick solution via AssemblyAI

import assemblyai as aai

aai.settings.api_key = ASSEMBLYAI_KEY

config = aai.TranscriptionConfig(
    language_code="ru",
    speaker_labels=True,         # диаризация
    punctuate=True,
    format_text=True,
    auto_chapters=True,          # автоглавы
    entity_detection=True,       # упоминания людей/компаний
    iab_categories=True,         # категоризация контента
)

transcriber = aai.Transcriber(config=config)
transcript = transcriber.transcribe("https://podcast.example.com/episode.mp3")

# Вывод с атрибуцией говорящих
for utterance in transcript.utterances:
    print(f"[Спикер {utterance.speaker}] {utterance.text}")

# Автоглавы
for chapter in transcript.chapters:
    print(f"## {chapter.headline} ({chapter.start//1000}с)")
    print(chapter.summary)
```### Self-hosted for long posts```python
from faster_whisper import WhisperModel

model = WhisperModel("large-v3", device="cuda", compute_type="int8_float16")

def transcribe_podcast(audio_path: str) -> str:
    segments, _ = model.transcribe(
        audio_path,
        language="ru",
        vad_filter=True,
        vad_parameters={"min_silence_duration_ms": 1000},
        word_timestamps=False,
        beam_size=5
    )
    return "\n".join(seg.text for seg in segments)
```For a 1-hour podcast: faster-whisper large-v3 on RTX 4090 = 15–18 minutes of processing. ### Export to various formats - **Markdown** with chapters — for publishing on the website - **SRT** — for adding subtitles to the video version - **PDF** — for downloading Timeframe: basic podcast transcription — 1–2 days. System with publishing and SEO optimization — 1 week.