Transcription

Transcribe audio to text using HuggingFace Whisper models.

Parameters

Parameter	Default	Description
--model	openai/whisper-large-v3	HuggingFace model repo ID
--revision	main	Model revision (branch, tag, or commit hash)
--cache-dir		HuggingFace cache directory for model files
--device	auto	Device to use (cuda, cpu, or auto)
--language		Source language code (e.g. en, de). Empty for auto-detect
--output-format	text	Output format: text, srt, or json
--batch-size	16	Batch size for processing audio chunks
--chunk-length-s	30.0	Length of audio chunks in seconds
--stride-length-s	5.0	Overlap between chunks in seconds
--return-timestamps	false	Return word/segment timestamps (required for SRT)

Supported Input Formats

Audio files supported by the Whisper pipeline (WAV, MP3, FLAC, OGG, etc.).

Output Format

Depends on --output-format: plain text (default), SRT subtitles, or JSON with timestamps. See examples below.

Models

Any HuggingFace automatic-speech-recognition model is supported. The OpenAI Whisper family is recommended.

Model	Params	Speed	License
openai/whisper-large-v3 (default)	1.5B	1x	Apache 2.0
openai/whisper-large-v3-turbo	809M	3x	MIT
openai/whisper-medium	769M	4x	MIT
openai/whisper-small	244M	9x	MIT
openai/whisper-base	74M	24x	MIT
openai/whisper-tiny	39M	50x	MIT

Tip

whisper-large-v3-turbo provides a good balance between accuracy and speed — nearly as accurate as large-v3 at roughly 3x the speed.

Examples

Transcribe audio to text

ConfigInputOutput (.txt)Output (.srt)Output (.json)

config.yaml

tasks:
  - name: transcribe
    kind: local
    module: tigerflow_ml.audio.transcribe.local
    input_ext: .mp3
    output_ext: .txt  # or .srt, .json
    params:
      # output_format: text   # (default) plain text
      # output_format: srt    # SRT subtitles (requires return_timestamps)
      # output_format: json   # raw Whisper output with timestamps
      # return_timestamps: true

An audio recording, e.g. lecture.mp3.

lecture.txt

Welcome to today's lecture on distributed computing. We will begin by
reviewing the fundamentals of parallel processing and then move on to
discuss fault tolerance in large-scale systems.

lecture.srt

1
00:00:00,000 --> 00:00:04,500
Welcome to today's lecture on distributed computing.

2
00:00:04,500 --> 00:00:09,200
We will begin by reviewing the fundamentals of parallel processing.

3
00:00:09,200 --> 00:00:14,800
And then move on to discuss fault tolerance in large-scale systems.

lecture.json

{
  "text": "Welcome to today's lecture on distributed computing...",
  "chunks": [
    {
      "text": "Welcome to today's lecture on distributed computing.",
      "timestamp": [0.0, 4.5]
    },
    {
      "text": "We will begin by reviewing the fundamentals of parallel processing.",
      "timestamp": [4.5, 9.2]
    }
  ]
}

Note

SRT and JSON output require return_timestamps: true.

Transcribe with language hint

Setting --language skips auto-detection and can improve accuracy when the source language is known:

config.yaml

tasks:
  - name: transcribe
    kind: local
    module: tigerflow_ml.audio.transcribe.local
    input_ext: .mp3
    output_ext: .txt
    params:
      language: en

Run on HPC with Slurm

For bulk transcription of large audio collections, use the Slurm variant to distribute work across compute nodes:

config.yaml

tasks:
  - name: transcribe
    kind: slurm
    module: tigerflow_ml.audio.transcribe.slurm
    input_ext: .mp3
    output_ext: .txt
    max_workers: 4
    worker_resources:
      cpus: 2
      gpus: 1
      memory: 16G
      time: 04:00:00