TensorRT-LLMs/examples/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.

• convert_checkpoint.py to convert weights from OpenAI Whisper format to TRT-LLM format.
• trtllm-build 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).

Support Matrix

• FP16
• INT8 (Weight Only Quant)
• INT4 (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

INFERENCE_PRECISION=float16
WEIGHT_ONLY_PRECISION=int8
MAX_BEAM_WIDTH=4
MAX_BATCH_SIZE=8
checkpoint_dir=whisper_large_v3_weights_${WEIGHT_ONLY_PRECISION}
output_dir=whisper_large_v3_${WEIGHT_ONLY_PRECISION}

# Convert the large-v3 model weights into TensorRT-LLM format.
python3 convert_checkpoint.py \
                --use_weight_only \
                --weight_only_precision $WEIGHT_ONLY_PRECISION \
                --output_dir $checkpoint_dir

# Build the large-v3 model using trtllm-build
trtllm-build  --checkpoint_dir ${checkpoint_dir}/encoder \
              --output_dir ${output_dir}/encoder \
              --moe_plugin disable \
              --enable_xqa disable \
              --max_batch_size ${MAX_BATCH_SIZE} \
              --gemm_plugin disable \
              --bert_attention_plugin ${INFERENCE_PRECISION} \
              --max_input_len 3000 --max_seq_len=3000

trtllm-build  --checkpoint_dir ${checkpoint_dir}/decoder \
              --output_dir ${output_dir}/decoder \
              --moe_plugin disable \
              --enable_xqa disable \
              --max_beam_width ${MAX_BEAM_WIDTH} \
              --max_batch_size ${MAX_BATCH_SIZE} \
              --max_seq_len 114 \
              --max_input_len 14 \
              --max_encoder_input_len 3000 \
              --gemm_plugin ${INFERENCE_PRECISION} \
              --bert_attention_plugin ${INFERENCE_PRECISION} \
              --gpt_attention_plugin ${INFERENCE_PRECISION}

Run

Different types of runtime are provided for whisper models. Following an order of serving performance and good usability, we recommend:

• (NEW) Python binding of C++ runtime w/ Paged KV Cache and Inflight Batching (IFB)
• Python runtime w/ Static Batching

Please refer to the documentation for the details of paged kv cache and inflight batching.

Run C++ runtime

Note: to use inflight batching and paged kv cache features in C++ runtime, please make sure you have set --paged_kv_cache enable and --remove_input_padding enable (which is by default enabled) in the trtllm-build command. Meanwhile, if using Python runtime, it is recommended to disable these flag by --paged_kv_cache disable and --remove_input_padding disable to avoid any unnecessary overhead.

# choose the engine you build [./whisper_large_v3, ./whisper_large_v3_int8]
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
# Inferencing via python binding of C++ runtime with inflight batching (IFB)
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

For pure C++ runtime, there is no example given yet. Please check the Executor API to implement your own end-to-end workflow. It is highly recommended to leverage more encapsulated solutions such as the above C++ Python binding or Triton backend.

Run Python runtime

For pure Python runtime, you can simply add the --use_py_session option.

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

# convert whisper model weights into TensorRT-LLM
INFERENCE_PRECISION=float16
WEIGHT_ONLY_PRECISION=int8
MAX_BEAM_WIDTH=4
MAX_BATCH_SIZE=8
checkpoint_dir=distil_whisper_medium_en_weights_${WEIGHT_ONLY_PRECISION}
output_dir=distil_whisper_medium_en${WEIGHT_ONLY_PRECISION}

python3 convert_checkpoint.py \
                --use_weight_only \
                --weight_only_precision $WEIGHT_ONLY_PRECISION \
                --output_dir $checkpoint_dir \
                --model_name distil-medium.en

Now, we can build and run the model like before:

trtllm-build  --checkpoint_dir ${checkpoint_dir}/encoder \
              --output_dir ${output_dir}/encoder \
              --moe_plugin disable \
              --enable_xqa disable \
              --max_batch_size ${MAX_BATCH_SIZE} \
              --gemm_plugin disable \
              --bert_attention_plugin ${INFERENCE_PRECISION} \
              --max_input_len 3000 --max_seq_len=3000

trtllm-build  --checkpoint_dir ${checkpoint_dir}/decoder \
              --output_dir ${output_dir}/decoder \
              --moe_plugin disable \
              --enable_xqa disable \
              --max_beam_width ${MAX_BEAM_WIDTH} \
              --max_batch_size ${MAX_BATCH_SIZE} \
              --max_seq_len 114 \
              --max_input_len 14 \
              --max_encoder_input_len 3000 \
              --gemm_plugin ${INFERENCE_PRECISION} \
              --bert_attention_plugin ${INFERENCE_PRECISION} \
              --gpt_attention_plugin ${INFERENCE_PRECISION}

# use cpp runtime python bindings
python3 run.py --engine_dir $output_dir --dataset hf-internal-testing/librispeech_asr_dummy --name librispeech_dummy_${output_dir}
# use python runtime
python3 run.py --engine_dir $output_dir --dataset hf-internal-testing/librispeech_asr_dummy --name librispeech_dummy_${output_dir} --use_py_session

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.