add voice assistant example (#8711)
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Yina Chen
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# Voice Assistant
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In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Whisper and Llama2 models on any Intel® Arc™ A-Series Graphics. For illustration purposes, we utilize the following models:
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- [openai/whisper-small](https://huggingface.co/openai/whisper-small) and [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) as reference whisper models.
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- [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) and [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) as reference Llama2 models.
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## 0. Requirements
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To run these examples with BigDL-LLM on Intel® Arc™ A-Series Graphics, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
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## Example: Predict Tokens using `generate()` API
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In the example [generate.py](./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 BigDL-LLM INT4 optimizations on Intel® Arc™ A-Series Graphics.
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### 1. Install
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We suggest using conda to manage environment:
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```bash
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conda create -n llm python=3.9
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conda activate llm
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# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
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# you can install specific ipex/torch version for your need
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pip install bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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pip install librosa soundfile datasets
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pip install accelerate
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pip install SpeechRecognition sentencepiece colorama
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```
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### 2. Configures OneAPI environment variables
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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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### 3. Run
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For optimal performance on Arc, it is recommended to set several environment variables.
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```bash
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export USE_XETLA=OFF
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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```
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```
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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
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```
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Arguments info:
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- `--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'`.
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- `--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'`.
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- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
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#### Sample Output
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Should be tested on a linux machine with microphone.
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import torch
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import time
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import argparse
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import numpy as np
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from bigdl.llm.transformers import AutoModelForCausalLM
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from bigdl.llm.transformers import AutoModelForSpeechSeq2Seq
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from transformers import LlamaTokenizer
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import intel_extension_for_pytorch as ipex
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from transformers import WhisperProcessor
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from transformers import TextStreamer
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from colorama import Fore
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import speech_recognition as sr
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from datasets import load_dataset
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# you could tune the prompt based on your own model,
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# here the prompt tuning refers to https://huggingface.co/georgesung/llama2_7b_chat_uncensored#prompt-style
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DEFAULT_SYSTEM_PROMPT = """\
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"""
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def get_prompt(message: str, chat_history: list[tuple[str, str]],
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system_prompt: str) -> str:
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texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
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# The first user input is _not_ stripped
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do_strip = False
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for user_input, response in chat_history:
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user_input = user_input.strip() if do_strip else user_input
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do_strip = True
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texts.append(f'{user_input} [/INST] {response.strip()} </s><s>[INST] ')
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message = message.strip() if do_strip else message
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texts.append(f'{message} [/INST]')
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return ''.join(texts)
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def get_input_features(r):
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with sr.Microphone(device_index=1, sample_rate=16000) as source:
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print("Calibrating...")
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r.adjust_for_ambient_noise(source, duration=5)
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print(Fore.YELLOW + "Listening now..." + Fore.RESET)
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try:
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audio = r.listen(source, timeout=5, phrase_time_limit=30)
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# refer to https://github.com/openai/whisper/blob/main/whisper/audio.py#L63
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frame_data = np.frombuffer(audio.frame_data, np.int16).flatten().astype(np.float32) / 32768.0
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input_features = processor(frame_data, sampling_rate=audio.sample_rate, return_tensors="pt").input_features
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input_features = input_features.half().contiguous().to('xpu')
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print("Recognizing...")
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except Exception as e:
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unrecognized_speech_text = (
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f"Sorry, I didn't catch that. Exception was: \n {e}"
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)
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print(unrecognized_speech_text)
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return input_features
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model')
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parser.add_argument('--llama2-repo-id-or-model-path', type=str, default="meta-llama/Llama-2-7b-chat-hf",
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help='The huggingface repo id for the Llama2 (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--whisper-repo-id-or-model-path', type=str, default="openai/whisper-small",
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help='The huggingface repo id for the Whisper (e.g. `openai/whisper-small` and `openai/whisper-medium`) to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--n-predict', type=int, default=32,
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help='Max tokens to predict')
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args = parser.parse_args()
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whisper_model_path = args.whisper_repo_id_or_model_path
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llama_model_path = args.llama2_repo_id_or_model_path
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dataset_path = "hf-internal-testing/librispeech_asr_dummy"
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# Load dummy dataset and read audio files
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ds = load_dataset(dataset_path, "clean", split="validation")
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print("Converting and loading models...")
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processor = WhisperProcessor.from_pretrained(whisper_model_path)
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# generate token ids
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whisper = AutoModelForSpeechSeq2Seq.from_pretrained(whisper_model_path, load_in_4bit=True, optimize_model=False)
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whisper.config.forced_decoder_ids = None
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whisper = whisper.half().to('xpu')
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llama_model = AutoModelForCausalLM.from_pretrained(llama_model_path, load_in_4bit=True, trust_remote_code=True, optimize_model=False)
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llama_model = llama_model.half().to('xpu')
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tokenizer = LlamaTokenizer.from_pretrained(llama_model_path)
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r = sr.Recognizer()
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with torch.inference_mode():
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# warm up
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sample = ds[2]["audio"]
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input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
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input_features = input_features.half().contiguous().to('xpu')
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torch.xpu.synchronize()
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predicted_ids = whisper.generate(input_features)
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torch.xpu.synchronize()
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output_str = processor.batch_decode(predicted_ids, skip_special_tokens=True)
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output_str = output_str[0]
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input_ids = tokenizer.encode(output_str, return_tensors="pt").to('xpu')
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output = llama_model.generate(input_ids, do_sample=False, max_new_tokens=32)
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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torch.xpu.synchronize()
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while 1:
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input_features = get_input_features(r)
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predicted_ids = whisper.generate(input_features)
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output_str = processor.batch_decode(predicted_ids, skip_special_tokens=True)
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output_str = output_str[0]
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print("\n" + Fore.GREEN + "Whisper : " + Fore.RESET + "\n" + output_str)
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print("\n" + Fore.BLUE + "BigDL-LLM: " + Fore.RESET)
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prompt = get_prompt(output_str, [], system_prompt=DEFAULT_SYSTEM_PROMPT)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
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streamer = TextStreamer(tokenizer, skip_special_tokens=True, skip_prompt=True)
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_ = llama_model.generate(input_ids, streamer=streamer, do_sample=False, max_new_tokens=args.n_predict)
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