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

pip install -U whisper-s2t-reborn

[!NOTE] load_model() defaults to device="cuda", which requires an NVIDIA GPU with CUDA 12.x and a CUDA-enabled build of PyTorch. The default torch wheel from PyPI on Windows is CPU-only — install the CUDA build explicitly:

pip install torch --index-url https://download.pytorch.org/whl/cu128

No NVIDIA GPU? See Running on CPU.

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})

Running on CPU

model = whisper_s2t.load_model("large-v3", device="cpu", compute_type="float32")

Pass compute_type="float32" — CTranslate2 does not run float16 on CPU. Smaller models (base, small) are much more practical for CPU inference.

Supported Models

All models are available in float16, float32, and bfloat16 compute types via CTranslate2-4you on Hugging Face.

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.

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.