# Humancto Indic Voice Pipeline Whisper Transcribe > Whisper Transcribe - Skill: `tomevault-io/humancto-indic-voice-pipeline-whisper-transcribe` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/humancto-indic-voice-pipeline-whisper-transcribe` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/humancto-indic-voice-pipeline-whisper-transcribe/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/humancto-indic-voice-pipeline-whisper-transcribe --- # Whisper Transcribe Transcribe and translate audio/video files locally using OpenAI Whisper. Supports 99 languages, runs entirely on your machine. ## Prerequisites Run once to install dependencies: ```bash pip install openai-whisper --quiet pip install transformers accelerate --quiet # For HuggingFace fine-tuned models ``` ffmpeg is required for audio processing: ```bash brew install ffmpeg # macOS ``` ## Step-by-Step Workflow **For ANY transcription/translation request, follow these steps:** ### Step 1: Check dependencies ```bash /usr/local/opt/python@3.11/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/check_deps.py ``` ### Step 2: Determine intent and run the appropriate command **User wants to transcribe audio/video to text:** ```bash /usr/local/opt/python@3.11/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py transcribe "" --output-dir ~/Downloads ``` **User wants to translate audio/video to English:** ```bash /usr/local/opt/python@3.11/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py translate "" --output-dir ~/Downloads ``` **User wants to detect the language:** ```bash /usr/local/opt/python@3.11/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py detect "" ``` **User wants file info without transcribing:** ```bash /usr/local/opt/python@3.11/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py info "" ``` ### Step 3: Report results Tell the user: - Detected language and confidence - The full transcription text - Where output files were saved (text, SRT subtitles, JSON) - Processing time - If translated: both original language and English translation ## All Commands ```bash # Transcribe audio/video (auto-detects language, saves .txt + .srt + .json) /usr/local/opt/python@3.11/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py transcribe "" --output-dir ~/Downloads # Transcribe with a specific source language (faster, skips detection) /usr/local/opt/python@3.11/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py transcribe "" --language te # Transcribe with a larger model for better accuracy /usr/local/opt/python@3.11/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py transcribe "" --model medium # Transcribe with a specific HuggingFace fine-tuned model /usr/local/opt/python@3.11/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py transcribe "" --hf-model "vasista22/whisper-telugu-large-v2" # Translate any language to English /usr/local/opt/python@3.11/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py translate "" --output-dir ~/Downloads # Translate with known source language /usr/local/opt/python@3.11/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py translate "" --language te # Detect language of audio /usr/local/opt/python@3.11/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py detect "" # Show audio file metadata /usr/local/opt/python@3.11/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py info "" ``` ## Indian Language Fine-Tuned Models The skill supports **12 Indian languages** with fine-tuned Whisper models from two sources: - **vasista22** (IIT Madras Speech Lab) — HuggingFace hosted, plug-and-play - **AI4Bharat IndicWhisper** — Downloaded as ZIP, cached locally at `~/.cache/indicwhisper/` **Auto-routing:** Just pass `--language ` — the best model is selected automatically: ```bash /usr/local/opt/python@3.11/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py transcribe "" --language te ``` **Manual override:** Use `--hf-model` to specify any HuggingFace Whisper model: ```bash /usr/local/opt/python@3.11/bin/python3.11 ~/.claude/skills/whisper-transcribe/scripts/whisper_transcribe.py transcribe "" --hf-model "vasista22/whisper-telugu-large-v2" ``` ### vasista22 Models (HuggingFace — auto-downloaded) | Language | Code | Model | | -------- | ---- | ----------------------------------- | | Telugu | `te` | `vasista22/whisper-telugu-large-v2` | | Hindi | `hi` | `vasista22/whisper-hindi-large-v2` | | Kannada | `kn` | `vasista22/whisper-kannada-medium` | | Gujarati | `gu` | `vasista22/whisper-gujarati-medium` | | Tamil | `ta` | `vasista22/whisper-tamil-medium` | Models by [vasista22 (IIT Madras Speech Lab)](https://huggingface.co/vasista22), funded by Bhashini / MeitY. ### AI4Bharat IndicWhisper Models (ZIP download — cached locally) These models are fine-tuned on Whisper-medium using the [Vistaar](https://github.com/AI4Bharat/vistaar) dataset. First use downloads the model ZIP (~500-800 MB) and caches it at `~/.cache/indicwhisper//`. | Language | Code | Source | | --------- | ---- | ------------------------ | | Bengali | `bn` | IndicWhisper (AI4Bharat) | | Malayalam | `ml` | IndicWhisper (AI4Bharat) | | Marathi | `mr` | IndicWhisper (AI4Bharat) | | Odia | `or` | IndicWhisper (AI4Bharat) | | Punjabi | `pa` | IndicWhisper (AI4Bharat) | | Sanskrit | `sa` | IndicWhisper (AI4Bharat) | | Urdu | `ur` | IndicWhisper (AI4Bharat) | Models by [AI4Bharat (IIT Madras)](https://ai4bharat.iitm.ac.in/), MIT licensed. ### Priority When a language has models from both sources (e.g. Hindi, Gujarati, Kannada, Tamil), the vasista22 HuggingFace model is preferred. IndicWhisper is used for languages not covered by vasista22. ## Model Sizes | Model | Size | Speed | Accuracy | Best for | | -------- | ------ | -------- | -------- | -------------------------------- | | `tiny` | 39 MB | Fastest | Low | Quick drafts, clear speech | | `base` | 74 MB | Fast | Good | Default — good balance | | `small` | 244 MB | Moderate | Better | Noisy audio, accented speech | | `medium` | 769 MB | Slow | Great | Non-English, complex audio | | `large` | 1.5 GB | Slowest | Best | Maximum accuracy, rare languages | ## Supported Languages (selection) English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic, Hindi, Telugu, Tamil, Bengali, Turkish, Ukrainian, Vietnamese, Thai, Indonesian, Swedish, and 70+ more. ## Important Notes - Default output location is `~/Downloads` - All output is JSON to stdout, status messages go to stderr - Three output files per transcription: `.txt` (plain text), `.srt` (subtitles), `.json` (structured) - Works with both audio files (mp3, wav, m4a, ogg, flac) and video files (mp4, mkv, webm, mov) - Video files have audio automatically extracted before transcription - Translation always outputs English (this is a Whisper limitation) - First run downloads the model (~74 MB for base) — subsequent runs use cache - Runs 100% locally — no internet needed after model download, no API keys - Use `--model medium` or `--model large` for better accuracy on non-English or noisy audio --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/humancto) — claim your Tome and manage your conversions.