whisper-s2t-reborn 1.4.6

A streamlined Speech-to-Text pipeline for Whisper using CTranslate2

WhisperS2T-Reborn ⚡

An Optimized Speech-to-Text Pipeline for the Whisper Model Using CTranslate2

WhisperS2T-Reborn is a modernized fork of WhisperS2T, an optimized lightning-fast Speech-to-Text (ASR) pipeline. It is tailored for the Whisper model using the CTranslate2 backend to provide faster transcription. It includes several heuristics to enhance transcription accuracy.

Whisper is a general-purpose speech recognition model developed by OpenAI. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification.

Installation

Recommended — install with PyAV for built-in audio decoding (no system dependencies required):

pip install -U whisper-s2t-reborn[pyav]

Alternatively, if you have ffmpeg correctly installed and available in your system PATH:

pip install -U whisper-s2t-reborn

Quick Start

Transcribe a single file

import whisper_s2t

model = whisper_s2t.load_model(model_identifier="large-v3")

files = ['audio1.wav']
lang_codes = ['en']
tasks = ['transcribe']
initial_prompts = [None]

out = model.transcribe_with_vad(files,
                                lang_codes=lang_codes,
                                tasks=tasks,
                                initial_prompts=initial_prompts,
                                batch_size=32)

print(out[0][0]) # Print first utterance for first file
"""
[Console Output]

{'text': "Let's bring in Phil Mackie who is there at the palace...",
 'avg_logprob': -0.25426941679184695,
 'no_speech_prob': 8.147954940795898e-05,
 'start_time': 0.0,
 'end_time': 24.8}
"""

Batch across multiple files

Passing multiple files allows segments from different files to be batched together, making better use of the GPU:

import whisper_s2t

model = whisper_s2t.load_model(model_identifier="large-v3")

files = ['audio1.wav', 'audio2.wav', 'audio3.wav']
lang_codes = ['en', 'en', 'en']
tasks = ['transcribe', 'transcribe', 'transcribe']
initial_prompts = [None, None, None]

out = model.transcribe_with_vad(files,
                                lang_codes=lang_codes,
                                tasks=tasks,
                                initial_prompts=initial_prompts,
                                batch_size=32)

# out[0] = results for audio1.wav, out[1] = results for audio2.wav, etc.
for file_idx, transcript in enumerate(out):
    print(f"File {files[file_idx]}: {len(transcript)} segments")

Word-level alignment

To enable word-level timestamps, load the model with:

model = whisper_s2t.load_model("large-v3", asr_options={'word_timestamps': True})

Benchmarks

Model: Whisper large-v3 · FP16 · CUDA · RTX 4090 Audio: sam_altman_lex_podcast_367.flac

Comparing openai-whisper (no batch support) against whisper-s2t-reborn.

Backend	Batch Size	Time (s)	Speedup	Inference VRAM (MB)
openai-whisper	1	508.5	1.0×	362
whisper-s2t-reborn	1	372.4	1.4×	560
whisper-s2t-reborn	2	239.6	2.1×	840
whisper-s2t-reborn	4	145.5	3.5×	1,387
whisper-s2t-reborn	8	95.5	5.3×	2,427
whisper-s2t-reborn	16	69.4	7.3×	4,608
whisper-s2t-reborn	32	57.1	8.9×	8,964

The increased VRAM usage even at batch size 1 is largely due to the VAD model. Openai's implementation doesn't use voice activity detection. The benchmarks folder has the actual scripts used.

VISUAL OF BENCHMARK RESULTS

Acknowledgements

• Original WhisperS2T: Thanks to shashig for the original WhisperS2T project that this fork is based on.
• OpenAI Whisper Team: Thanks to the OpenAI Whisper Team for open-sourcing the Whisper model.
• CTranslate2 Team: Thanks to the CTranslate2 Team for providing a faster inference engine for Transformers architecture.
• NVIDIA NeMo Team: Thanks to the NVIDIA NeMo Team for their contribution of the open-source VAD model used in this pipeline.

License

This project is licensed under MIT License - see the LICENSE file for details.