Fix voice assistant example input error on Linux (#8799)
* fix linux error * update * remove alsa log
This commit is contained in:
Yina Chen
GitHub
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9537194b4b
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7c37424a63
2 changed files with 105 additions and 38 deletions
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@ -19,7 +19,10 @@ pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-w
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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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# If you failed to install PyAudio, try to run sudo apt install portaudio19-dev on ubuntu
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pip install PyAudio inquirer sounddevice
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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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@ -44,4 +47,55 @@ Arguments info:
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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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```bash
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(llm) bigdl@bigdl-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
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/home/bigdl/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?
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warn(
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[?] Which microphone do you choose?: Default
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> Default
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HDA Intel PCH: ALC274 Analog (hw:0,0)
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HDA Intel PCH: HDMI 0 (hw:0,3)
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HDA Intel PCH: HDMI 1 (hw:0,7)
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HDA Intel PCH: HDMI 2 (hw:0,8)
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HDA Intel PCH: HDMI 3 (hw:0,9)
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HDA Intel PCH: HDMI 4 (hw:0,10)
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HDA Intel PCH: HDMI 5 (hw:0,11)
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HDA Intel PCH: HDMI 6 (hw:0,12)
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HDA Intel PCH: HDMI 7 (hw:0,13)
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HDA Intel PCH: HDMI 8 (hw:0,14)
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HDA Intel PCH: HDMI 9 (hw:0,15)
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HDA Intel PCH: HDMI 10 (hw:0,16)
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The device name Default is selected.
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Downloading builder script: 100%|██████████████████████████████████████████████████████| 5.17k/5.17k [00:00<00:00, 14.3MB/s]
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Downloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████| 9.08M/9.08M [00:01<00:00, 4.75MB/s]
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Downloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00, 4.57s/it]]
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Extracting data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 39.98it/s]
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Generating validation split: 73 examples [00:00, 5328.37 examples/s]
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Converting and loading models...
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Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:09<00:00, 3.04s/it]
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/home/bigdl/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.
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warnings.warn(
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/home/bigdl/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.
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warnings.warn(
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/home/bigdl/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 )
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warnings.warn(
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Calibrating...
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Listening now...
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Recognizing...
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Whisper :
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What is AI?
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BigDL-LLM:
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Artificial intelligence (AI) is the broader field of research and development aimed at creating machines that can perform tasks that typically require human intelligence,
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Listening now...
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Recognizing...
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Whisper :
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Tell me something about Intel
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BigDL-LLM:
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Intel is a well-known technology company that specializes in designing, manufacturing, and selling computer hardware components and semiconductor products.
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```
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@ -17,13 +17,15 @@
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import os
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import torch
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import time
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import intel_extension_for_pytorch as ipex
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import argparse
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import numpy as np
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import inquirer
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import sounddevice
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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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@ -49,27 +51,6 @@ def get_prompt(message: str, chat_history: list[tuple[str, str]],
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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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@ -82,6 +63,21 @@ if __name__ == '__main__':
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help='Max tokens to predict')
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args = parser.parse_args()
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# Select device
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mics = sr.Microphone.list_microphone_names()
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mics.insert(0, "Default")
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questions = [
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inquirer.List('device_name',
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message="Which microphone do you choose?",
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choices=mics)
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]
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answers = inquirer.prompt(questions)
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device_name = answers['device_name']
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idx = mics.index(device_name)
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device_index = None if idx == 0 else idx - 1
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print(f"The device name {device_name} is selected.")
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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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@ -95,10 +91,10 @@ if __name__ == '__main__':
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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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whisper = whisper.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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llama_model = llama_model.to('xpu')
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tokenizer = LlamaTokenizer.from_pretrained(llama_model_path)
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r = sr.Recognizer()
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@ -107,7 +103,7 @@ if __name__ == '__main__':
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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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input_features = input_features.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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@ -118,14 +114,31 @@ if __name__ == '__main__':
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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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with sr.Microphone(device_index=device_index, 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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while 1:
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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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print("Recognizing...")
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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.contiguous().to('xpu')
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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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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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