Insanely Fast Whisper

Powered by 🤗 Transformers, Optimum & flash-attn

TL;DR - Transcribe 150 minutes (2.5 hours) of audio in less than 98 seconds - with OpenAI's Whisper Large v3. Blazingly fast transcription is now a reality!⚡️

Not convinced? Here are some benchmarks we ran on a free Google Colab T4 GPU! 👇

Optimisation type	Time to Transcribe (150 mins of Audio)
Transformers (fp32)	~31 (31 min 1 sec)
Transformers (fp16 + batching [24] + bettertransformer)	~5 (5 min 2 sec)
Transformers (fp16 + batching [24] + Flash Attention 2)	~2 (1 min 38 sec)
distil-whisper (fp16 + batching [24] + bettertransformer)	~3 (3 min 16 sec)
distil-whisper (fp16 + batching [24] + Flash Attention 2)	~1 (1 min 18 sec)
Faster Whisper (fp16 + beam_size [1])	~9.23 (9 min 23 sec)
Faster Whisper (8-bit + beam_size [1])	~8 (8 min 15 sec)

🆕 Blazingly fast transcriptions via your terminal! ⚡️

We've added a CLI to enable fast transcriptions. Here's how you can use it:

Install insanely-fast-whisper with pipx:

pipx install insanely-fast-whisper

Run inference from any path on your computer:

insanely-fast-whisper --file-name <filename or URL>

🔥 You can run Whisper-large-v3 w/ Flash Attention 2 from this CLI too:

insanely-fast-whisper --file-name <filename or URL> --flash True 

🌟 You can run distil-whisper directly from this CLI too:

insanely-fast-whisper --model-name distil-whisper/large-v2 --file-name <filename or URL> 

Don't want to install insanely-fast-whisper? Just use pipx run:

pipx run insanely-fast-whisper --file-name <filename or URL>

Note: The CLI is opinionated and currently only works for Nvidia GPUs. Make sure to check out the defaults and the list of options you can play around with to maximise your transcription throughput. Run insanely-fast-whisper --help or pipx run insanely-fast-whisper --help to get all the CLI arguments and defaults.

How to use it without a CLI?

For older GPUs, all you need to run is:

import torch
from transformers import pipeline

pipe = pipeline("automatic-speech-recognition",
                "openai/whisper-large-v2",
                torch_dtype=torch.float16,
                device="cuda:0")

pipe.model = pipe.model.to_bettertransformer()

outputs = pipe("<FILE_NAME>",
               chunk_length_s=30,
               batch_size=24,
               return_timestamps=True)

outputs["text"]

For newer (A10, A100, H100s), use Flash Attention:

import torch
from transformers import pipeline

pipe = pipeline("automatic-speech-recognition",
                "openai/whisper-large-v2",
                torch_dtype=torch.float16,
                model_kwargs={"use_flash_attention_2": True},
                device="cuda:0")

outputs = pipe("<FILE_NAME>",
               chunk_length_s=30,
               batch_size=24,
               return_timestamps=True)

outputs["text"]                

Roadmap

• Add a light CLI script
• Deployment script with Inference API

Community showcase

@ochen1 created a brilliant MVP for a CLI here: https://github.com/ochen1/insanely-fast-whisper-cli (Try it out now!)