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https://github.com/Vaibhavs10/insanely-fast-whisper


https://github.com/Vaibhavs10/insanely-fast-whisper

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# Insanely Fast Whisper



An opinionated CLI to transcribe Audio files w/ Whisper on-device! 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](https://huggingface.co/openai/whisper-large-v3). Blazingly fast transcription is now a reality!⚡️



```

pipx install insanely-fast-whisper==0.0.15 --force

```










Not convinced? Here are some benchmarks we ran on a Nvidia A100 - 80GB 👇



| Optimisation type    | Time to Transcribe (150 mins of Audio) |

|------------------|------------------|

| large-v3 (Transformers) (`fp32`)             | ~31 (*31 min 1 sec*)             |

| large-v3 (Transformers) (`fp16` + `batching [24]` + `bettertransformer`) | ~5 (*5 min 2 sec*)            |

| **large-v3 (Transformers) (`fp16` + `batching [24]` + `Flash Attention 2`)** | **~2 (*1 min 38 sec*)**            |

| distil-large-v2 (Transformers) (`fp16` + `batching [24]` + `bettertransformer`) | ~3 (*3 min 16 sec*)            |

| **distil-large-v2 (Transformers) (`fp16` + `batching [24]` + `Flash Attention 2`)** | **~1 (*1 min 18 sec*)**           |

| large-v2 (Faster Whisper) (`fp16` + `beam_size [1]`) | ~9.23 (*9 min 23 sec*)            |

| large-v2 (Faster Whisper) (`8-bit` + `beam_size [1]`) | ~8 (*8 min 15 sec*)            |



P.S. We also ran the benchmarks on a [Google Colab T4 GPU](/notebooks/) instance too!



P.P.S. This project originally started as a way to showcase benchmarks for Transformers, but has since evolved into a lightweight CLI for people to use. This is purely community driven. We add whatever community seems to have a strong demand for! 



## 🆕 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` (`pip install pipx` or `brew install pipx`):



```bash

pipx install insanely-fast-whisper

```



⚠️ If you have python 3.11.XX installed, `pipx` may parse the version incorrectly and install a very old version of `insanely-fast-whisper` without telling you (version `0.0.8`, which won't work anymore with the current `BetterTransformers`). In that case, you can install the latest version by passing `--ignore-requires-python` to `pip`:



```bash

pipx install insanely-fast-whisper --force --pip-args="--ignore-requires-python"

```



If you're installing with `pip`, you can pass the argument directly: `pip install insanely-fast-whisper --ignore-requires-python`.



Run inference from any path on your computer:



```bash

insanely-fast-whisper --file-name 

```

*Note: if you are running on macOS, you also need to add `--device-id mps` flag.*



🔥 You can run [Whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) w/ [Flash Attention 2](https://github.com/Dao-AILab/flash-attention) from this CLI too:



```bash

insanely-fast-whisper --file-name  --flash True 

```



🌟 You can run [distil-whisper](https://huggingface.co/distil-whisper) directly from this CLI too:



```bash

insanely-fast-whisper --model-name distil-whisper/large-v2 --file-name  

```



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



```bash

pipx run insanely-fast-whisper --file-name 

```



> [!NOTE]

> The CLI is highly opinionated and only works on NVIDIA GPUs & Mac. 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 along with their defaults. 



## CLI Options



The `insanely-fast-whisper` repo provides an all round support for running Whisper in various settings. Note that as of today 26th Nov, `insanely-fast-whisper` works on both CUDA and mps (mac) enabled devices.

```

  -h, --help            show this help message and exit

  --file-name FILE_NAME

                        Path or URL to the audio file to be transcribed.

  --device-id DEVICE_ID

                        Device ID for your GPU. Just pass the device number when using CUDA, or "mps" for Macs with Apple Silicon. (default: "0")

  --transcript-path TRANSCRIPT_PATH

                        Path to save the transcription output. (default: output.json)

  --model-name MODEL_NAME

                        Name of the pretrained model/ checkpoint to perform ASR. (default: openai/whisper-large-v3)

  --task {transcribe,translate}

                        Task to perform: transcribe or translate to another language. (default: transcribe)

  --language LANGUAGE   

                        Language of the input audio. (default: "None" (Whisper auto-detects the language))

  --batch-size BATCH_SIZE

                        Number of parallel batches you want to compute. Reduce if you face OOMs. (default: 24)

  --flash FLASH         

                        Use Flash Attention 2. Read the FAQs to see how to install FA2 correctly. (default: False)

  --timestamp {chunk,word}

                        Whisper supports both chunked as well as word level timestamps. (default: chunk)

  --hf-token HF_TOKEN

                        Provide a hf.co/settings/token for Pyannote.audio to diarise the audio clips

  --diarization_model DIARIZATION_MODEL

                        Name of the pretrained model/ checkpoint to perform diarization. (default: pyannote/speaker-diarization)

  --num-speakers NUM_SPEAKERS

                        Specifies the exact number of speakers present in the audio file. Useful when the exact number of participants in the conversation is known. Must be at least 1. Cannot be used together with --min-speakers or --max-speakers. (default: None)

  --min-speakers MIN_SPEAKERS

                        Sets the minimum number of speakers that the system should consider during diarization. Must be at least 1. Cannot be used together with --num-speakers. Must be less than or equal to --max-speakers if both are specified. (default: None)

  --max-speakers MAX_SPEAKERS

                        Defines the maximum number of speakers that the system should consider in diarization. Must be at least 1. Cannot be used together with --num-speakers. Must be greater than or equal to --min-speakers if both are specified. (default: None)

```



## Frequently Asked Questions



**How to correctly install flash-attn to make it work with `insanely-fast-whisper`?**



Make sure to install it via `pipx runpip insanely-fast-whisper install flash-attn --no-build-isolation`. Massive kudos to @li-yifei for helping with this.



**How to solve an `AssertionError: Torch not compiled with CUDA enabled` error on Windows?**



The root cause of this problem is still unknown, however, you can resolve this by manually installing torch in the virtualenv like `python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121`. Thanks to @pto2k for all tdebugging this.



**How to avoid Out-Of-Memory (OOM) exceptions on Mac?**



The *mps* backend isn't as optimised as CUDA, hence is way more memory hungry. Typically you can run with `--batch-size 4` without any issues (should use roughly 12GB GPU VRAM). Don't forget to set `--device-id mps`.



## How to use Whisper without a CLI?



All you need to run is the below snippet:



```

pip install --upgrade transformers optimum accelerate

```



```python

import torch

from transformers import pipeline

from transformers.utils import is_flash_attn_2_available



pipe = pipeline(

    "automatic-speech-recognition",

    model="openai/whisper-large-v3", # select checkpoint from https://huggingface.co/openai/whisper-large-v3#model-details

    torch_dtype=torch.float16,

    device="cuda:0", # or mps for Mac devices

    model_kwargs={"attn_implementation": "flash_attention_2"} if is_flash_attn_2_available() else {"attn_implementation": "sdpa"},

)



outputs = pipe(

    "",

    chunk_length_s=30,

    batch_size=24,

    return_timestamps=True,

)



outputs

```



## Acknowledgements



1. [OpenAI Whisper](https://github.com/openai/whisper) team for open sourcing such a brilliant check point.

2. Hugging Face Transformers team, specifically [Arthur](https://github.com/ArthurZucker), [Patrick](https://github.com/patrickvonplaten), [Sanchit](https://github.com/sanchit-gandhi) & [Yoach](https://github.com/ylacombe)  (alphabetical order) for continuing to maintain Whisper in Transformers.

3. Hugging Face [Optimum](https://github.com/huggingface/optimum) team for making the BetterTransformer API so easily accessible.

4. [Patrick Arminio](https://github.com/patrick91) for helping me tremendously to put together this CLI.



## Community showcase



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

2. @arihanv created an app (Shush) using NextJS (Frontend) & Modal (Backend): https://github.com/arihanv/Shush (Check it outtt!)

3. @kadirnar created a python package on top of the transformers with optimisations: https://github.com/kadirnar/whisper-plus (Go go go!!!)