| --- |
| viewer: false |
| tags: |
| - uv-script |
| - audio |
| - transcription |
| - automatic-speech-recognition |
| private: true |
| --- |
| |
| # Transcription |
|
|
| Scripts for transcribing audio files using HF Buckets and Jobs. |
|
|
| ## Quick Start |
|
|
| Scripts run directly from their Hub URL — no clone or local checkout needed: |
|
|
| ```bash |
| # 1. Download audio from Internet Archive straight into a bucket |
| hf jobs uv run \ |
| -v hf://buckets/user/audio-files:/output \ |
| https://huggingface.co/datasets/uv-scripts/transcription/raw/main/download-ia.py \ |
| SUSPENSE /output |
| |
| # 2. Transcribe — audio bucket in, transcript bucket out |
| hf jobs uv run --flavor l4x1 -s HF_TOKEN \ |
| -e UV_TORCH_BACKEND=cu128 \ |
| -v hf://buckets/user/audio-files:/input:ro \ |
| -v hf://buckets/user/transcripts:/output \ |
| https://huggingface.co/datasets/uv-scripts/transcription/raw/main/cohere-transcribe.py \ |
| /input /output --language en --compile |
| ``` |
|
|
| No download/upload step. Buckets are mounted directly as volumes via [hf-mount](https://github.com/huggingface/hf-mount). |
|
|
| > **Local dev**: if you've cloned this repo, swap the URL for the local filename (e.g. `cohere-transcribe.py /input /output ...`). |
|
|
| ## Scripts |
|
|
| ### Transcription |
|
|
| | Script | Model | Backend | Output | Speed | |
| |--------|-------|---------|--------|-------| |
| | `cohere-transcribe.py` | Cohere Transcribe (2B) | transformers | `.txt` | 161x RT (A100) | |
| | `cohere-transcribe-vllm.py` | Cohere Transcribe (2B) | vLLM nightly | `.txt` | 214x RT (A100) | |
| | `easytranscriber-transcribe.py` | Cohere Transcribe 2B (default) or Whisper variants | [easytranscriber](https://github.com/kb-labb/easytranscriber) | JSON word timestamps (+ optional `.txt` / `.srt`) | 42.9x RT (L4) | |
|
|
| **`cohere-transcribe.py`** (recommended for plain text) — uses `model.transcribe()` with automatic long-form chunking, overlap, and reassembly. Stable dependencies. |
|
|
| **`cohere-transcribe-vllm.py`** — experimental vLLM variant. Faster but requires nightly vLLM and has minor duplication at chunk boundaries. |
|
|
| **`easytranscriber-transcribe.py`** — when you need **word-level timestamps** (subtitles, search indexing, forced alignment). Runs VAD → ASR → wav2vec2 emissions → forced alignment. Defaults to the Cohere backend so you get the same model as the other scripts with alignment on top; swap to `--backend ct2` + a Whisper model for languages Cohere doesn't cover (e.g. Swedish via `KBLab/kb-whisper-large`). |
|
|
| #### Options — `cohere-transcribe.py` / `cohere-transcribe-vllm.py` |
|
|
| | Flag | Default | Description | |
| |------|---------|-------------| |
| | `--language` | required | en, de, fr, it, es, pt, el, nl, pl, ar, vi, zh, ja, ko | |
| | `--compile` | off | torch.compile encoder (one-time warmup, faster after) | |
| | `--batch-size` | 16 | Batch size for inference | |
| | `--max-files` | all | Limit files to process (for testing) | |
|
|
| #### Options — `easytranscriber-transcribe.py` |
|
|
| | Flag | Default | Description | |
| |------|---------|-------------| |
| | `--language` | required | ISO 639-1 code. Cohere supports the same 14 languages as above; ct2/hf support any Whisper language | |
| | `--backend` | `cohere` | `cohere`, `ct2` (CTranslate2 Whisper, fastest for Whisper), or `hf` (transformers) | |
| | `--transcription-model` | Cohere 2B / distil-whisper-large-v3.5 | HF model ID; override to use KB-Whisper, Whisper-large-v3, etc. | |
| | `--emissions-model` | per-language default | wav2vec2 for forced alignment: en→`wav2vec2-base-960h`, sv→`voxrex-swedish`, else→`facebook/mms-1b-all` | |
| | `--vad` | `silero` | `silero` (no auth) or `pyannote` (requires accepting terms + HF_TOKEN) | |
| | `--tokenizer-lang` | derived from `--language` | NLTK Punkt language name for sentence tokenization | |
| | `--emit-txt` | off | Also write `.txt` transcripts alongside the JSON alignments | |
| | `--emit-srt` | off | Also write `.srt` subtitles derived from alignment segments | |
| | `--batch-size-features` | 8 | Feature-extraction batch size | |
| | `--batch-size-transcribe` | 16 | ASR batch size (where backend supports it) | |
| | `--max-files` | all | Limit files to process (for testing) | |
| |
| #### Benchmarks |
| |
| CBS Suspense (1940s radio drama), 66 episodes, 33 hours of audio. |
| |
| **`cohere-transcribe.py`** (plain text): |
| |
| | GPU | Time | RTFx | |
| |-----|------|------| |
| | A100-SXM4-80GB | 12.3 min | 161x realtime | |
| | L4 | ~64s / 30 min episode | 28x realtime | |
| |
| **`easytranscriber-transcribe.py`** (JSON alignments + optional .txt/.srt; VAD → ASR → wav2vec2 → forced alignment): |
| |
| | GPU | Time | RTFx | Output | |
| |-----|------|------|--------| |
| | L4 | 46.2 min | 42.9x realtime | 66 JSON + SRT + TXT (42,633 segments, 295k words) | |
| |
| ### Data |
| |
| | Script | Description | |
| |--------|-------------| |
| | `download-ia.py` | Download audio from Internet Archive into a mounted bucket | |
| |
| ## Notes |
| |
| - **Gated model**: Accept terms at the [model page](https://huggingface.co/CohereLabs/cohere-transcribe-03-2026) before use. |
| - **Tokenizer workaround**: `cohere-transcribe.py` applies a one-line patch for a tokenizer compat issue. Will be removed once upstream fixes land ([model discussion](https://huggingface.co/CohereLabs/cohere-transcribe-03-2026/discussions/11)). |
| - **easytranscriber**: the Cohere backend requires `transformers>=5.4.0` (pinned in the script). Pyannote VAD is gated — accept terms at [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) and [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) if using `--vad pyannote`. Otherwise stick with the default Silero VAD. |
| |
