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.py applies 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.