ipex-llm/python/llm/example/GPU/HF-Transformers-AutoModels/Model/voiceassistant

Voice Assistant

In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Whisper and Llama2 models on Intel GPUs. For illustration purposes, we utilize the following models:

• openai/whisper-small and openai/whisper-medium as reference whisper models.
• meta-llama/Llama-2-7b-chat-hf and meta-llama/Llama-2-13b-chat-hf as reference Llama2 models.

0. Requirements

To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to here for more information.

Example: Predict Tokens using generate() API

In the example generate.py, we show a basic use case for a Whisper model to conduct transcription using generate() API, then use the recoginzed text as the input for Llama2 model to predict the next N tokens using generate() API, with IPEX-LLM INT4 optimizations on Intel GPUs.

1. Install

1.1 Installation on Linux

We suggest using conda to manage environment:

conda create -n llm python=3.9
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
pip install librosa soundfile datasets
pip install accelerate
pip install SpeechRecognition sentencepiece colorama
# If you failed to install PyAudio, try to run sudo apt install portaudio19-dev on ubuntu
pip install PyAudio inquirer sounddevice

1.2 Installation on Windows

We suggest using conda to manage environment:

conda create -n llm python=3.9 libuv
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
pip install librosa soundfile datasets
pip install accelerate
pip install SpeechRecognition sentencepiece colorama
pip install PyAudio inquirer

2. Configures OneAPI environment variables

2.1 Configurations for Linux

source /opt/intel/oneapi/setvars.sh

2.2 Configurations for Windows

call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"

Note: Please make sure you are using CMD (Anaconda Prompt if using conda) to run the command as PowerShell is not supported.

3. Runtime Configurations

For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.

3.1 Configurations for Linux

For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series

export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1

For Intel Data Center GPU Max Series

export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export ENABLE_SDP_FUSION=1

Note: Please note that libtcmalloc.so can be installed by conda install -c conda-forge -y gperftools=2.10.

3.2 Configurations for Windows

For Intel iGPU

set SYCL_CACHE_PERSISTENT=1
set BIGDL_LLM_XMX_DISABLED=1

For Intel Arc™ A300-Series or Pro A60

set SYCL_CACHE_PERSISTENT=1

For other Intel dGPU Series

There is no need to set further environment variables.

Note: For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.

4. Running examples

python ./generate.py --llama2-repo-id-or-model-path REPO_ID_OR_MODEL_PATH --whisper-repo-id-or-model-path REPO_ID_OR_MODEL_PATH --n-predict N_PREDICT

Arguments info:

• --llama2-repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the Llama2 model (e.g. meta-llama/Llama-2-7b-chat-hf and meta-llama/Llama-2-13b-chat-hf) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'meta-llama/Llama-2-7b-chat-hf'.
• --whisper-repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the Whisper model (e.g. openai/whisper-small and openai/whisper-medium) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'openai/whisper-small'.
• --n-predict N_PREDICT: argument defining the max number of tokens to predict. It is default to be 32.

Known Issues

The speech_recognition library may occasionally skip recording due to low volume. An alternative option is to save the recording in WAV format using PyAudio and read the file as an input. Here is an example using PyAudio:

import pyaudio
import speech_recognition as sr

CHUNK = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1                # The desired number of input channels
RATE = 16000                # The desired rate (in Hz)
RECORD_SECONDS = 10         # Recording time (in second)
WAVE_OUTPUT_FILENAME = "/path/to/pyaudio_out.wav"
p = pyaudio.PyAudio()
                
stream = p.open(format=FORMAT,
                channels=CHANNELS,
                rate=RATE,
                input=True,
                frames_per_buffer=CHUNK)

print("*"*10, "Listening\n")
frames = []
data =0
for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
  data = stream.read(CHUNK)  ## <class 'bytes'> ,exception_on_overflow = False
  frames.append(data)   ## <class 'list'>
print("*"*10, "Stop recording\n")

stream.stop_stream()
stream.close()
p.terminate()

wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
wf.setnchannels(CHANNELS)
wf.setsampwidth(p.get_sample_size(FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(frames))
wf.close()

r = sr.Recognizer()
with sr.AudioFile(WAVE_OUTPUT_FILENAME) as source1:
    audio = r.record(source1)  # read the entire audio file   
frame_data = np.frombuffer(audio.frame_data, np.int16).flatten().astype(np.float32) / 32768.0

Sample Output

(llm) ipex@ipex-llm:~/Documents/voiceassistant$ python generate.py --llama2-repo-id-or-model-path /mnt/windows/demo/models/Llama-2-7b-chat-hf --whisper-repo-id-or-model-path /mnt/windows/demo/models/whisper-medium
/home/ipex/anaconda3/envs/llm/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: ''If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?
  warn(

[?] Which microphone do you choose?: Default
 > Default
   HDA Intel PCH: ALC274 Analog (hw:0,0)
   HDA Intel PCH: HDMI 0 (hw:0,3)
   HDA Intel PCH: HDMI 1 (hw:0,7)
   HDA Intel PCH: HDMI 2 (hw:0,8)
   HDA Intel PCH: HDMI 3 (hw:0,9)
   HDA Intel PCH: HDMI 4 (hw:0,10)
   HDA Intel PCH: HDMI 5 (hw:0,11)
   HDA Intel PCH: HDMI 6 (hw:0,12)
   HDA Intel PCH: HDMI 7 (hw:0,13)
   HDA Intel PCH: HDMI 8 (hw:0,14)
   HDA Intel PCH: HDMI 9 (hw:0,15)
   HDA Intel PCH: HDMI 10 (hw:0,16)

The device name Default is selected.
Downloading builder script: 100%|██████████████████████████████████████████████████████| 5.17k/5.17k [00:00<00:00, 14.3MB/s]
Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████| 9.08M/9.08M [00:01<00:00, 4.75MB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00,  4.57s/it]]
Extracting data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 39.98it/s]
Generating validation split: 73 examples [00:00, 5328.37 examples/s]
Converting and loading models...
Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:09<00:00,  3.04s/it]
/home/ipex/anaconda3/envs/yina-llm/lib/python3.9/site-packages/transformers/generation/configuration_utils.py:362: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/ipex/anaconda3/envs/yina-llm/lib/python3.9/site-packages/transformers/generation/configuration_utils.py:367: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/ipex/anaconda3/envs/yina-llm/lib/python3.9/site-packages/transformers/generation/utils.py:1411: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )
  warnings.warn(
Calibrating...
Listening now...
Recognizing...

Whisper : 
 What is AI?

IPEX-LLM: 
 Artificial intelligence (AI) is the broader field of research and development aimed at creating machines that can perform tasks that typically require human intelligence,
Listening now...
Recognizing...

Whisper : 
 Tell me something about Intel

IPEX-LLM: 
 Intel is a well-known technology company that specializes in designing, manufacturing, and selling computer hardware components and semiconductor products.