whisper

Whisper

This document shows how to build and run a whisper model in TensorRT-LLM on a single GPU.

• Whisper
  • Overview
  • Support Matrix
  • Usage
    • Build TensorRT engine(s)
    • Run
    • Distil-Whisper
    • Acknowledgment

Overview

The TensorRT-LLM Whisper example code is located in examples/whisper. There are three main files in that folder:

• build.py to build the TensorRT engine(s) needed to run the Whisper model.
• run.py to run the inference on a single wav file, or a HuggingFace dataset (Librispeech test clean).
• run_faster_whisper.py to do benchmark comparison with Faster Whisper.

Support Matrix

• FP16
• INT8 (Weight Only Quant)

Usage

The TensorRT-LLM Whisper example code locates at examples/whisper. It takes whisper pytorch weights as input, and builds the corresponding TensorRT engines.

Build TensorRT engine(s)

Need to prepare the whisper checkpoint first by downloading models from here.

wget --directory-prefix=assets https://raw.githubusercontent.com/openai/whisper/main/whisper/assets/multilingual.tiktoken
wget --directory-prefix=assets assets/mel_filters.npz https://raw.githubusercontent.com/openai/whisper/main/whisper/assets/mel_filters.npz
wget --directory-prefix=assets https://raw.githubusercontent.com/yuekaizhang/Triton-ASR-Client/main/datasets/mini_en/wav/1221-135766-0002.wav
# take large-v3 model as an example
wget --directory-prefix=assets https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt

TensorRT-LLM Whisper builds TensorRT engine(s) from the pytorch checkpoint.

# install requirements first
pip install -r requirements.txt

# Build the large-v3 model using a single GPU with plugins.
python3 build.py --output_dir whisper_large_v3 --use_gpt_attention_plugin --use_gemm_plugin  --use_bert_attention_plugin --enable_context_fmha

# Build the large-v3 model using a single GPU with plugins and int8 weight-only quantization.
python3 build.py --output_dir whisper_large_v3_weight_only --use_gpt_attention_plugin --use_gemm_plugin  --use_bert_attention_plugin --enable_context_fmha --use_weight_only

Run

# choose the engine you build [./whisper_large_v3, ./whisper_large_weight_only]
output_dir=./whisper_large_v3
# decode a single audio file
# If the input file does not have a .wav extension, ffmpeg needs to be installed with the following command:
# apt-get update && apt-get install -y ffmpeg
python3 run.py --name single_wav_test --engine_dir $output_dir --input_file assets/1221-135766-0002.wav
# decode a whole dataset
python3 run.py --engine_dir $output_dir --dataset hf-internal-testing/librispeech_asr_dummy --enable_warmup --name librispeech_dummy_large_v3_plugin

Distil-Whisper

TensorRT-LLM also supports using distil-whisper's different models by first converting their params and weights from huggingface's naming format to openai whisper naming format. You can do so by running the script distil_whisper/convert_from_distil_whisper.py as follows:

# take distil-medium.en as an example
# download the gpt2.tiktoken
wget --directory-prefix=assets https://raw.githubusercontent.com/openai/whisper/main/whisper/assets/gpt2.tiktoken

# will download the model weights from huggingface and convert them to openai-whisper's pytorch format
# model is saved to ./assets/ by default
python3 distil_whisper/convert_from_distil_whisper.py --model_name distil-whisper/distil-medium.en --output_name distil-medium.en

# now we can build and run the model like before:
output_dir=distil_whisper_medium_en
python3 build.py --model_name distil-medium.en --output_dir $output_dir --use_gpt_attention_plugin --use_gemm_plugin --use_bert_attention_plugin --enable_context_fmha

python3 run.py --engine_dir $output_dir --dataset hf-internal-testing/librispeech_asr_dummy --name librispeech_dummy_${output_dir} --tokenizer_name gpt2

Acknowledgment

This implementation of TensorRT-LLM for Whisper has been adapted from the NVIDIA TensorRT-LLM Hackathon 2023 submission of Jinheng Wang, which can be found in the repository Eddie-Wang-Hackathon2023 on GitHub. We extend our gratitude to Jinheng for providing a foundation for the implementation.