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:
# 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.
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 | 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 before use.
- Tokenizer workaround:
cohere-transcribe.pyapplies a one-line patch for a tokenizer compat issue. Will be removed once upstream fixes land (model discussion). - easytranscriber: the Cohere backend requires
transformers>=5.4.0(pinned in the script). Pyannote VAD is gated — accept terms at pyannote/segmentation-3.0 and pyannote/speaker-diarization-3.1 if using--vad pyannote. Otherwise stick with the default Silero VAD.
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