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Running
on
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Running
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Zero
Upload 8 files
Browse files- .gitattributes +1 -0
- README.md +30 -15
- abc2xml.py +0 -0
- config.py +15 -0
- demo.py +236 -0
- illustration.png +3 -0
- inference.py +260 -0
- prompts.txt +112 -0
- utils.py +406 -0
.gitattributes
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@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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examples/web_6f93090a-81f6-489e-bb35-1a2838b18c01.png filter=lfs diff=lfs merge=lfs -text
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examples/web_dfacd48d-d2c2-492f-b94c-41e6a34ea99f.png filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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examples/web_6f93090a-81f6-489e-bb35-1a2838b18c01.png filter=lfs diff=lfs merge=lfs -text
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examples/web_dfacd48d-d2c2-492f-b94c-41e6a34ea99f.png filter=lfs diff=lfs merge=lfs -text
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illustration.png filter=lfs diff=lfs merge=lfs -text
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README.md
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title: DeepSeek-R1
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emoji: 🐋
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colorFrom: indigo
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colorTo: blue
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sdk: gradio
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sdk_version: 5.12.0
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app_file: app.py
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pinned: false
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preload_from_hub:
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
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short_description: Try out the distilled DeepSeek-R1 models (MIT licensed!)
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---
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## Local Gradio Demo
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1. Set up the environment:
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```
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conda create --name notagen python=3.10
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conda activate notagen
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conda install pytorch==2.3.0 pytorch-cuda=11.8 -c pytorch -c nvidia
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pip install accelerate
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pip install optimum
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pip install -r requirements.txt
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```
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2. Download [NotaGen-X](https://huggingface.co/ElectricAlexis/NotaGen/blob/main/weights_notagenx_p_size_16_p_length_1024_p_layers_20_h_size_1280.pth) and put it under ```gradio/```.
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3. run ```demo.py```:
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```
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cd gradio/
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python demo.py
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```
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4. Then you can view the demo page at 0.0.0.0:7861.
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<p align="center">
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<img src="illustration.png" alt="NotaGen Gradio Demo">
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</p>
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You can choose period, composer, and instrumentation as a prompt combination for NotaGen's conditional generation. After generation completes, you can save the ABC notation and MusicXML files locally.
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It is with some regret that the current combination of prompts is limited to 112, which is constrained by the number of pieces of music under each prompt in the fine-tuning dataset. We hope to expand the combinations and forms of prompts in the future.
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abc2xml.py
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The diff for this file is too large to render.
See raw diff
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config.py
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import os
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# Configurations for inference
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INFERENCE_WEIGHTS_PATH = 'weights_notagenx_p_size_16_p_length_1024_p_layers_20_h_size_1280.pth' # Path to weights for inference# Folder to save output files
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TOP_K = 9 # Top k for sampling
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TOP_P = 0.9 # Top p for sampling
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TEMPERATURE = 1.2 # Temperature for sampling
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# Configurations for model
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PATCH_STREAM = True # Stream training / inference
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PATCH_SIZE = 16 # Patch Size
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PATCH_LENGTH = 1024 # Patch Length
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CHAR_NUM_LAYERS = 6 # Number of layers in the decoder
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PATCH_NUM_LAYERS = 20 # Number of layers in the encoder
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HIDDEN_SIZE = 1280 # Hidden Size
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demo.py
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import gradio as gr
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import sys
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import threading
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import queue
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from io import TextIOBase
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from inference import inference_patch
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import datetime
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import subprocess
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import os
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# Predefined valid combinations set
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with open('prompts.txt', 'r') as f:
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prompts = f.readlines()
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valid_combinations = set()
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for prompt in prompts:
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prompt = prompt.strip()
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parts = prompt.split('_')
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valid_combinations.add((parts[0], parts[1], parts[2]))
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# Generate available options
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periods = sorted({p for p, _, _ in valid_combinations})
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composers = sorted({c for _, c, _ in valid_combinations})
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instruments = sorted({i for _, _, i in valid_combinations})
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# Dynamic component updates
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def update_components(period, composer):
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if not period:
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return [
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gr.Dropdown(choices=[], value=None, interactive=False),
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gr.Dropdown(choices=[], value=None, interactive=False)
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]
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valid_composers = sorted({c for p, c, _ in valid_combinations if p == period})
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valid_instruments = sorted({i for p, c, i in valid_combinations if p == period and c == composer}) if composer else []
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return [
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gr.Dropdown(
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choices=valid_composers,
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value=composer if composer in valid_composers else None,
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interactive=True
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),
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gr.Dropdown(
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choices=valid_instruments,
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value=None,
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interactive=bool(valid_instruments)
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)
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]
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class RealtimeStream(TextIOBase):
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def __init__(self, queue):
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self.queue = queue
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def write(self, text):
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self.queue.put(text)
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return len(text)
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def save_and_convert(abc_content, period, composer, instrumentation):
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if not all([period, composer, instrumentation]):
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raise gr.Error("Please complete a valid generation first before saving")
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timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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prompt_str = f"{period}_{composer}_{instrumentation}"
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filename_base = f"{timestamp}_{prompt_str}"
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abc_filename = f"{filename_base}.abc"
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with open(abc_filename, "w", encoding="utf-8") as f:
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f.write(abc_content)
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xml_filename = f"{filename_base}.xml"
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try:
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subprocess.run(
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["python", "abc2xml.py", '-o', '.', abc_filename, ],
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check=True,
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capture_output=True,
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text=True
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)
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except subprocess.CalledProcessError as e:
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error_msg = f"Conversion failed: {e.stderr}" if e.stderr else "Unknown error"
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raise gr.Error(f"ABC to XML conversion failed: {error_msg}. Please try to generate another composition.")
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return f"Saved successfully: {abc_filename} -> {xml_filename}"
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def generate_music(period, composer, instrumentation):
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if (period, composer, instrumentation) not in valid_combinations:
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raise gr.Error("Invalid prompt combination! Please re-select from the period options")
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output_queue = queue.Queue()
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original_stdout = sys.stdout
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sys.stdout = RealtimeStream(output_queue)
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result_container = []
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def run_inference():
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try:
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result_container.append(inference_patch(period, composer, instrumentation))
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finally:
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sys.stdout = original_stdout
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thread = threading.Thread(target=run_inference)
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thread.start()
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process_output = ""
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while thread.is_alive():
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try:
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text = output_queue.get(timeout=0.1)
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process_output += text
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yield process_output, None
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except queue.Empty:
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continue
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while not output_queue.empty():
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text = output_queue.get()
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process_output += text
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yield process_output, None
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final_result = result_container[0] if result_container else ""
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yield process_output, final_result
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with gr.Blocks() as demo:
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gr.Markdown("## NotaGen")
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with gr.Row():
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# 左侧栏
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with gr.Column():
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period_dd = gr.Dropdown(
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choices=periods,
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value=None,
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label="Period",
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interactive=True
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)
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composer_dd = gr.Dropdown(
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choices=[],
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value=None,
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label="Composer",
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interactive=False
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)
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instrument_dd = gr.Dropdown(
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choices=[],
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value=None,
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label="Instrumentation",
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interactive=False
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)
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generate_btn = gr.Button("Generate!", variant="primary")
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process_output = gr.Textbox(
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label="Generation process",
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interactive=False,
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lines=15,
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max_lines=15,
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placeholder="Generation progress will be shown here...",
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elem_classes="process-output"
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)
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# 右侧栏
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with gr.Column():
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final_output = gr.Textbox(
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label="Post-processed ABC notation scores",
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interactive=True,
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lines=23,
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placeholder="Post-processed ABC scores will be shown here...",
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elem_classes="final-output"
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)
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with gr.Row():
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save_btn = gr.Button("💾 Save as ABC & XML files", variant="secondary")
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save_status = gr.Textbox(
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label="Save Status",
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interactive=False,
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visible=True,
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max_lines=2
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)
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period_dd.change(
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update_components,
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inputs=[period_dd, composer_dd],
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outputs=[composer_dd, instrument_dd]
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)
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composer_dd.change(
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update_components,
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inputs=[period_dd, composer_dd],
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outputs=[composer_dd, instrument_dd]
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)
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generate_btn.click(
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generate_music,
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inputs=[period_dd, composer_dd, instrument_dd],
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outputs=[process_output, final_output]
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)
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save_btn.click(
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save_and_convert,
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inputs=[final_output, period_dd, composer_dd, instrument_dd],
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outputs=[save_status]
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)
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css = """
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.process-output {
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background-color: #f0f0f0;
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font-family: monospace;
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padding: 10px;
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border-radius: 5px;
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}
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.final-output {
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background-color: #ffffff;
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font-family: sans-serif;
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padding: 10px;
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border-radius: 5px;
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}
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.process-output textarea {
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max-height: 500px !important;
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overflow-y: auto !important;
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white-space: pre-wrap;
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}
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"""
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css += """
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button#💾-save-convert:hover {
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background-color: #ffe6e6;
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}
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"""
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demo.css = css
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if __name__ == "__main__":
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demo.launch(
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server_name="0.0.0.0",
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server_port=7861
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)
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illustration.png
ADDED
![]() |
Git LFS Details
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inference.py
ADDED
@@ -0,0 +1,260 @@
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|
1 |
+
|
2 |
+
import os
|
3 |
+
import time
|
4 |
+
import torch
|
5 |
+
from utils import *
|
6 |
+
from config import *
|
7 |
+
from transformers import GPT2Config, LlamaConfig
|
8 |
+
from abctoolkit.utils import Exclaim_re, Quote_re, SquareBracket_re, Barline_regexPattern
|
9 |
+
from abctoolkit.transpose import Note_list, Pitch_sign_list
|
10 |
+
from abctoolkit.duration import calculate_bartext_duration
|
11 |
+
|
12 |
+
Note_list = Note_list + ['z', 'x']
|
13 |
+
|
14 |
+
if torch.cuda.is_available():
|
15 |
+
device = torch.device("cuda")
|
16 |
+
else:
|
17 |
+
device = torch.device("cpu")
|
18 |
+
|
19 |
+
patchilizer = Patchilizer()
|
20 |
+
|
21 |
+
patch_config = GPT2Config(num_hidden_layers=PATCH_NUM_LAYERS,
|
22 |
+
max_length=PATCH_LENGTH,
|
23 |
+
max_position_embeddings=PATCH_LENGTH,
|
24 |
+
n_embd=HIDDEN_SIZE,
|
25 |
+
num_attention_heads=HIDDEN_SIZE // 64,
|
26 |
+
vocab_size=1)
|
27 |
+
byte_config = GPT2Config(num_hidden_layers=CHAR_NUM_LAYERS,
|
28 |
+
max_length=PATCH_SIZE + 1,
|
29 |
+
max_position_embeddings=PATCH_SIZE + 1,
|
30 |
+
hidden_size=HIDDEN_SIZE,
|
31 |
+
num_attention_heads=HIDDEN_SIZE // 64,
|
32 |
+
vocab_size=128)
|
33 |
+
|
34 |
+
model = NotaGenLMHeadModel(encoder_config=patch_config, decoder_config=byte_config)
|
35 |
+
|
36 |
+
print("Parameter Number: " + str(sum(p.numel() for p in model.parameters() if p.requires_grad)))
|
37 |
+
|
38 |
+
checkpoint = torch.load(INFERENCE_WEIGHTS_PATH, map_location=torch.device(device))
|
39 |
+
model.load_state_dict(checkpoint['model'])
|
40 |
+
model = model.to(device)
|
41 |
+
model.eval()
|
42 |
+
|
43 |
+
|
44 |
+
def rest_unreduce(abc_lines):
|
45 |
+
|
46 |
+
tunebody_index = None
|
47 |
+
for i in range(len(abc_lines)):
|
48 |
+
if '[V:' in abc_lines[i]:
|
49 |
+
tunebody_index = i
|
50 |
+
break
|
51 |
+
|
52 |
+
metadata_lines = abc_lines[: tunebody_index]
|
53 |
+
tunebody_lines = abc_lines[tunebody_index:]
|
54 |
+
|
55 |
+
part_symbol_list = []
|
56 |
+
voice_group_list = []
|
57 |
+
for line in metadata_lines:
|
58 |
+
if line.startswith('%%score'):
|
59 |
+
for round_bracket_match in re.findall(r'\((.*?)\)', line):
|
60 |
+
voice_group_list.append(round_bracket_match.split())
|
61 |
+
existed_voices = [item for sublist in voice_group_list for item in sublist]
|
62 |
+
if line.startswith('V:'):
|
63 |
+
symbol = line.split()[0]
|
64 |
+
part_symbol_list.append(symbol)
|
65 |
+
if symbol[2:] not in existed_voices:
|
66 |
+
voice_group_list.append([symbol[2:]])
|
67 |
+
z_symbol_list = [] # voices that use z as rest
|
68 |
+
x_symbol_list = [] # voices that use x as rest
|
69 |
+
for voice_group in voice_group_list:
|
70 |
+
z_symbol_list.append('V:' + voice_group[0])
|
71 |
+
for j in range(1, len(voice_group)):
|
72 |
+
x_symbol_list.append('V:' + voice_group[j])
|
73 |
+
|
74 |
+
part_symbol_list.sort(key=lambda x: int(x[2:]))
|
75 |
+
|
76 |
+
unreduced_tunebody_lines = []
|
77 |
+
|
78 |
+
for i, line in enumerate(tunebody_lines):
|
79 |
+
unreduced_line = ''
|
80 |
+
|
81 |
+
line = re.sub(r'^\[r:[^\]]*\]', '', line)
|
82 |
+
|
83 |
+
pattern = r'\[V:(\d+)\](.*?)(?=\[V:|$)'
|
84 |
+
matches = re.findall(pattern, line)
|
85 |
+
|
86 |
+
line_bar_dict = {}
|
87 |
+
for match in matches:
|
88 |
+
key = f'V:{match[0]}'
|
89 |
+
value = match[1]
|
90 |
+
line_bar_dict[key] = value
|
91 |
+
|
92 |
+
# calculate duration and collect barline
|
93 |
+
dur_dict = {}
|
94 |
+
for symbol, bartext in line_bar_dict.items():
|
95 |
+
right_barline = ''.join(re.split(Barline_regexPattern, bartext)[-2:])
|
96 |
+
bartext = bartext[:-len(right_barline)]
|
97 |
+
try:
|
98 |
+
bar_dur = calculate_bartext_duration(bartext)
|
99 |
+
except:
|
100 |
+
bar_dur = None
|
101 |
+
if bar_dur is not None:
|
102 |
+
if bar_dur not in dur_dict.keys():
|
103 |
+
dur_dict[bar_dur] = 1
|
104 |
+
else:
|
105 |
+
dur_dict[bar_dur] += 1
|
106 |
+
|
107 |
+
try:
|
108 |
+
ref_dur = max(dur_dict, key=dur_dict.get)
|
109 |
+
except:
|
110 |
+
pass # use last ref_dur
|
111 |
+
|
112 |
+
if i == 0:
|
113 |
+
prefix_left_barline = line.split('[V:')[0]
|
114 |
+
else:
|
115 |
+
prefix_left_barline = ''
|
116 |
+
|
117 |
+
for symbol in part_symbol_list:
|
118 |
+
if symbol in line_bar_dict.keys():
|
119 |
+
symbol_bartext = line_bar_dict[symbol]
|
120 |
+
else:
|
121 |
+
if symbol in z_symbol_list:
|
122 |
+
symbol_bartext = prefix_left_barline + 'z' + str(ref_dur) + right_barline
|
123 |
+
elif symbol in x_symbol_list:
|
124 |
+
symbol_bartext = prefix_left_barline + 'x' + str(ref_dur) + right_barline
|
125 |
+
unreduced_line += '[' + symbol + ']' + symbol_bartext
|
126 |
+
|
127 |
+
unreduced_tunebody_lines.append(unreduced_line + '\n')
|
128 |
+
|
129 |
+
unreduced_lines = metadata_lines + unreduced_tunebody_lines
|
130 |
+
|
131 |
+
return unreduced_lines
|
132 |
+
|
133 |
+
|
134 |
+
def inference_patch(period, composer, instrumentation):
|
135 |
+
|
136 |
+
prompt_lines=[
|
137 |
+
'%' + period + '\n',
|
138 |
+
'%' + composer + '\n',
|
139 |
+
'%' + instrumentation + '\n']
|
140 |
+
|
141 |
+
while True:
|
142 |
+
|
143 |
+
failure_flag = False
|
144 |
+
|
145 |
+
bos_patch = [patchilizer.bos_token_id] * (PATCH_SIZE - 1) + [patchilizer.eos_token_id]
|
146 |
+
|
147 |
+
start_time = time.time()
|
148 |
+
|
149 |
+
prompt_patches = patchilizer.patchilize_metadata(prompt_lines)
|
150 |
+
byte_list = list(''.join(prompt_lines))
|
151 |
+
print(''.join(byte_list), end='')
|
152 |
+
|
153 |
+
prompt_patches = [[ord(c) for c in patch] + [patchilizer.special_token_id] * (PATCH_SIZE - len(patch)) for patch
|
154 |
+
in prompt_patches]
|
155 |
+
prompt_patches.insert(0, bos_patch)
|
156 |
+
|
157 |
+
input_patches = torch.tensor(prompt_patches, device=device).reshape(1, -1)
|
158 |
+
|
159 |
+
end_flag = False
|
160 |
+
cut_index = None
|
161 |
+
|
162 |
+
tunebody_flag = False
|
163 |
+
|
164 |
+
while True:
|
165 |
+
predicted_patch = model.generate(input_patches.unsqueeze(0),
|
166 |
+
top_k=TOP_K,
|
167 |
+
top_p=TOP_P,
|
168 |
+
temperature=TEMPERATURE)
|
169 |
+
if not tunebody_flag and patchilizer.decode([predicted_patch]).startswith('[r:'): # start with [r:0/
|
170 |
+
tunebody_flag = True
|
171 |
+
r0_patch = torch.tensor([ord(c) for c in '[r:0/']).unsqueeze(0).to(device)
|
172 |
+
temp_input_patches = torch.concat([input_patches, r0_patch], axis=-1)
|
173 |
+
predicted_patch = model.generate(temp_input_patches.unsqueeze(0),
|
174 |
+
top_k=TOP_K,
|
175 |
+
top_p=TOP_P,
|
176 |
+
temperature=TEMPERATURE)
|
177 |
+
predicted_patch = [ord(c) for c in '[r:0/'] + predicted_patch
|
178 |
+
if predicted_patch[0] == patchilizer.bos_token_id and predicted_patch[1] == patchilizer.eos_token_id:
|
179 |
+
end_flag = True
|
180 |
+
break
|
181 |
+
next_patch = patchilizer.decode([predicted_patch])
|
182 |
+
|
183 |
+
for char in next_patch:
|
184 |
+
byte_list.append(char)
|
185 |
+
print(char, end='')
|
186 |
+
|
187 |
+
patch_end_flag = False
|
188 |
+
for j in range(len(predicted_patch)):
|
189 |
+
if patch_end_flag:
|
190 |
+
predicted_patch[j] = patchilizer.special_token_id
|
191 |
+
if predicted_patch[j] == patchilizer.eos_token_id:
|
192 |
+
patch_end_flag = True
|
193 |
+
|
194 |
+
predicted_patch = torch.tensor([predicted_patch], device=device) # (1, 16)
|
195 |
+
input_patches = torch.cat([input_patches, predicted_patch], dim=1) # (1, 16 * patch_len)
|
196 |
+
|
197 |
+
if len(byte_list) > 102400:
|
198 |
+
failure_flag = True
|
199 |
+
break
|
200 |
+
if time.time() - start_time > 20 * 60:
|
201 |
+
failure_flag = True
|
202 |
+
break
|
203 |
+
|
204 |
+
if input_patches.shape[1] >= PATCH_LENGTH * PATCH_SIZE and not end_flag:
|
205 |
+
print('Stream generating...')
|
206 |
+
abc_code = ''.join(byte_list)
|
207 |
+
abc_lines = abc_code.split('\n')
|
208 |
+
|
209 |
+
tunebody_index = None
|
210 |
+
for i, line in enumerate(abc_lines):
|
211 |
+
if line.startswith('[r:') or line.startswith('[V:'):
|
212 |
+
tunebody_index = i
|
213 |
+
break
|
214 |
+
if tunebody_index is None or tunebody_index == len(abc_lines) - 1:
|
215 |
+
break
|
216 |
+
|
217 |
+
metadata_lines = abc_lines[:tunebody_index]
|
218 |
+
tunebody_lines = abc_lines[tunebody_index:]
|
219 |
+
|
220 |
+
metadata_lines = [line + '\n' for line in metadata_lines]
|
221 |
+
if not abc_code.endswith('\n'):
|
222 |
+
tunebody_lines = [tunebody_lines[i] + '\n' for i in range(len(tunebody_lines) - 1)] + [
|
223 |
+
tunebody_lines[-1]]
|
224 |
+
else:
|
225 |
+
tunebody_lines = [tunebody_lines[i] + '\n' for i in range(len(tunebody_lines))]
|
226 |
+
|
227 |
+
if cut_index is None:
|
228 |
+
cut_index = len(tunebody_lines) // 2
|
229 |
+
|
230 |
+
abc_code_slice = ''.join(metadata_lines + tunebody_lines[-cut_index:])
|
231 |
+
input_patches = patchilizer.encode_generate(abc_code_slice)
|
232 |
+
|
233 |
+
input_patches = [item for sublist in input_patches for item in sublist]
|
234 |
+
input_patches = torch.tensor([input_patches], device=device)
|
235 |
+
input_patches = input_patches.reshape(1, -1)
|
236 |
+
|
237 |
+
if not failure_flag:
|
238 |
+
abc_text = ''.join(byte_list)
|
239 |
+
|
240 |
+
# unreduce
|
241 |
+
abc_lines = abc_text.split('\n')
|
242 |
+
abc_lines = list(filter(None, abc_lines))
|
243 |
+
abc_lines = [line + '\n' for line in abc_lines]
|
244 |
+
try:
|
245 |
+
unreduced_abc_lines = rest_unreduce(abc_lines)
|
246 |
+
except:
|
247 |
+
failure_flag = True
|
248 |
+
pass
|
249 |
+
else:
|
250 |
+
unreduced_abc_lines = [line for line in unreduced_abc_lines if not(line.startswith('%') and not line.startswith('%%'))]
|
251 |
+
unreduced_abc_lines = ['X:1\n'] + unreduced_abc_lines
|
252 |
+
unreduced_abc_text = ''.join(unreduced_abc_lines)
|
253 |
+
return unreduced_abc_text
|
254 |
+
|
255 |
+
|
256 |
+
|
257 |
+
|
258 |
+
|
259 |
+
if __name__ == '__main__':
|
260 |
+
inference_patch('Classical', 'Beethoven, Ludwig van', 'Keyboard')
|
prompts.txt
ADDED
@@ -0,0 +1,112 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Baroque_Bach, Johann Sebastian_Chamber
|
2 |
+
Baroque_Bach, Johann Sebastian_Choral
|
3 |
+
Baroque_Bach, Johann Sebastian_Keyboard
|
4 |
+
Baroque_Bach, Johann Sebastian_Orchestral
|
5 |
+
Baroque_Bach, Johann Sebastian_Vocal-Orchestral
|
6 |
+
Baroque_Corelli, Arcangelo_Chamber
|
7 |
+
Baroque_Corelli, Arcangelo_Orchestral
|
8 |
+
Baroque_Handel, George Frideric_Chamber
|
9 |
+
Baroque_Handel, George Frideric_Keyboard
|
10 |
+
Baroque_Handel, George Frideric_Orchestral
|
11 |
+
Baroque_Handel, George Frideric_Vocal-Orchestral
|
12 |
+
Baroque_Scarlatti, Domenico_Keyboard
|
13 |
+
Baroque_Vivaldi, Antonio_Chamber
|
14 |
+
Baroque_Vivaldi, Antonio_Orchestral
|
15 |
+
Baroque_Vivaldi, Antonio_Vocal-Orchestral
|
16 |
+
Classical_Beethoven, Ludwig van_Art Song
|
17 |
+
Classical_Beethoven, Ludwig van_Chamber
|
18 |
+
Classical_Beethoven, Ludwig van_Keyboard
|
19 |
+
Classical_Beethoven, Ludwig van_Orchestral
|
20 |
+
Classical_Haydn, Joseph_Chamber
|
21 |
+
Classical_Haydn, Joseph_Keyboard
|
22 |
+
Classical_Haydn, Joseph_Orchestral
|
23 |
+
Classical_Haydn, Joseph_Vocal-Orchestral
|
24 |
+
Classical_Mozart, Wolfgang Amadeus_Chamber
|
25 |
+
Classical_Mozart, Wolfgang Amadeus_Choral
|
26 |
+
Classical_Mozart, Wolfgang Amadeus_Keyboard
|
27 |
+
Classical_Mozart, Wolfgang Amadeus_Orchestral
|
28 |
+
Classical_Mozart, Wolfgang Amadeus_Vocal-Orchestral
|
29 |
+
Classical_Paradis, Maria Theresia von_Art Song
|
30 |
+
Classical_Reichardt, Louise_Art Song
|
31 |
+
Classical_Saint-Georges, Joseph Bologne_Chamber
|
32 |
+
Classical_Schroter, Corona_Art Song
|
33 |
+
Romantic_Bartok, Bela_Keyboard
|
34 |
+
Romantic_Berlioz, Hector_Choral
|
35 |
+
Romantic_Bizet, Georges_Art Song
|
36 |
+
Romantic_Boulanger, Lili_Art Song
|
37 |
+
Romantic_Boulton, Harold_Art Song
|
38 |
+
Romantic_Brahms, Johannes_Art Song
|
39 |
+
Romantic_Brahms, Johannes_Chamber
|
40 |
+
Romantic_Brahms, Johannes_Choral
|
41 |
+
Romantic_Brahms, Johannes_Keyboard
|
42 |
+
Romantic_Brahms, Johannes_Orchestral
|
43 |
+
Romantic_Burgmuller, Friedrich_Keyboard
|
44 |
+
Romantic_Butterworth, George_Art Song
|
45 |
+
Romantic_Chaminade, Cecile_Art Song
|
46 |
+
Romantic_Chausson, Ernest_Art Song
|
47 |
+
Romantic_Chopin, Frederic_Art Song
|
48 |
+
Romantic_Chopin, Frederic_Keyboard
|
49 |
+
Romantic_Cornelius, Peter_Art Song
|
50 |
+
Romantic_Debussy, Claude_Art Song
|
51 |
+
Romantic_Debussy, Claude_Keyboard
|
52 |
+
Romantic_Dvorak, Antonin_Chamber
|
53 |
+
Romantic_Dvorak, Antonin_Choral
|
54 |
+
Romantic_Dvorak, Antonin_Keyboard
|
55 |
+
Romantic_Dvorak, Antonin_Orchestral
|
56 |
+
Romantic_Faisst, Clara_Art Song
|
57 |
+
Romantic_Faure, Gabriel_Art Song
|
58 |
+
Romantic_Faure, Gabriel_Chamber
|
59 |
+
Romantic_Faure, Gabriel_Keyboard
|
60 |
+
Romantic_Franz, Robert_Art Song
|
61 |
+
Romantic_Gonzaga, Chiquinha_Art Song
|
62 |
+
Romantic_Grandval, Clemence de_Art Song
|
63 |
+
Romantic_Grieg, Edvard_Keyboard
|
64 |
+
Romantic_Grieg, Edvard_Orchestral
|
65 |
+
Romantic_Hensel, Fanny_Art Song
|
66 |
+
Romantic_Holmes, Augusta Mary Anne_Art Song
|
67 |
+
Romantic_Jaell, Marie_Art Song
|
68 |
+
Romantic_Kinkel, Johanna_Art Song
|
69 |
+
Romantic_Kralik, Mathilde_Art Song
|
70 |
+
Romantic_Lang, Josephine_Art Song
|
71 |
+
Romantic_Lehmann, Liza_Art Song
|
72 |
+
Romantic_Liszt, Franz_Keyboard
|
73 |
+
Romantic_Mayer, Emilie_Chamber
|
74 |
+
Romantic_Medtner, Nikolay_Keyboard
|
75 |
+
Romantic_Mendelssohn, Felix_Art Song
|
76 |
+
Romantic_Mendelssohn, Felix_Chamber
|
77 |
+
Romantic_Mendelssohn, Felix_Choral
|
78 |
+
Romantic_Mendelssohn, Felix_Keyboard
|
79 |
+
Romantic_Mendelssohn, Felix_Orchestral
|
80 |
+
Romantic_Munktell, Helena_Art Song
|
81 |
+
Romantic_Parratt, Walter_Choral
|
82 |
+
Romantic_Prokofiev, Sergey_Keyboard
|
83 |
+
Romantic_Rachmaninoff, Sergei_Choral
|
84 |
+
Romantic_Rachmaninoff, Sergei_Keyboard
|
85 |
+
Romantic_Ravel, Maurice_Art Song
|
86 |
+
Romantic_Ravel, Maurice_Chamber
|
87 |
+
Romantic_Ravel, Maurice_Keyboard
|
88 |
+
Romantic_Saint-Saens, Camille_Chamber
|
89 |
+
Romantic_Saint-Saens, Camille_Keyboard
|
90 |
+
Romantic_Saint-Saens, Camille_Orchestral
|
91 |
+
Romantic_Satie, Erik_Art Song
|
92 |
+
Romantic_Satie, Erik_Keyboard
|
93 |
+
Romantic_Schubert, Franz_Art Song
|
94 |
+
Romantic_Schubert, Franz_Chamber
|
95 |
+
Romantic_Schubert, Franz_Choral
|
96 |
+
Romantic_Schubert, Franz_Keyboard
|
97 |
+
Romantic_Schumann, Clara_Art Song
|
98 |
+
Romantic_Schumann, Robert_Art Song
|
99 |
+
Romantic_Schumann, Robert_Chamber
|
100 |
+
Romantic_Schumann, Robert_Choral
|
101 |
+
Romantic_Schumann, Robert_Keyboard
|
102 |
+
Romantic_Scriabin, Aleksandr_Keyboard
|
103 |
+
Romantic_Shostakovich, Dmitry_Chamber
|
104 |
+
Romantic_Shostakovich, Dmitry_Keyboard
|
105 |
+
Romantic_Sibelius, Jean_Keyboard
|
106 |
+
Romantic_Smetana, Bedrich_Keyboard
|
107 |
+
Romantic_Tchaikovsky, Pyotr_Keyboard
|
108 |
+
Romantic_Tchaikovsky, Pyotr_Orchestral
|
109 |
+
Romantic_Viardot, Pauline_Art Song
|
110 |
+
Romantic_Warlock, Peter_Art Song
|
111 |
+
Romantic_Wolf, Hugo_Art Song
|
112 |
+
Romantic_Zumsteeg, Emilie_Art Song
|
utils.py
ADDED
@@ -0,0 +1,406 @@
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|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import random
|
3 |
+
import bisect
|
4 |
+
import json
|
5 |
+
import re
|
6 |
+
from config import *
|
7 |
+
from transformers import GPT2Model, GPT2LMHeadModel, LlamaModel, LlamaForCausalLM, PreTrainedModel
|
8 |
+
from samplings import top_p_sampling, top_k_sampling, temperature_sampling
|
9 |
+
from tokenizers import Tokenizer
|
10 |
+
|
11 |
+
|
12 |
+
class Patchilizer:
|
13 |
+
def __init__(self, stream=PATCH_STREAM):
|
14 |
+
self.stream = stream
|
15 |
+
self.delimiters = ["|:", "::", ":|", "[|", "||", "|]", "|"]
|
16 |
+
self.regexPattern = '(' + '|'.join(map(re.escape, self.delimiters)) + ')'
|
17 |
+
self.bos_token_id = 1
|
18 |
+
self.eos_token_id = 2
|
19 |
+
self.special_token_id = 0
|
20 |
+
|
21 |
+
def split_bars(self, body_lines):
|
22 |
+
"""
|
23 |
+
Split a body of music into individual bars.
|
24 |
+
"""
|
25 |
+
new_bars = []
|
26 |
+
try:
|
27 |
+
for line in body_lines:
|
28 |
+
line_bars = re.split(self.regexPattern, line)
|
29 |
+
line_bars = list(filter(None, line_bars))
|
30 |
+
new_line_bars = []
|
31 |
+
|
32 |
+
if len(line_bars) == 1:
|
33 |
+
new_line_bars = line_bars
|
34 |
+
else:
|
35 |
+
if line_bars[0] in self.delimiters:
|
36 |
+
new_line_bars = [line_bars[i] + line_bars[i + 1] for i in range(0, len(line_bars), 2)]
|
37 |
+
else:
|
38 |
+
new_line_bars = [line_bars[0]] + [line_bars[i] + line_bars[i + 1] for i in range(1, len(line_bars), 2)]
|
39 |
+
if 'V' not in new_line_bars[-1]:
|
40 |
+
new_line_bars[-2] += new_line_bars[-1] # 吸收最后一个 小节线+\n 的组合
|
41 |
+
new_line_bars = new_line_bars[:-1]
|
42 |
+
new_bars += new_line_bars
|
43 |
+
except:
|
44 |
+
pass
|
45 |
+
|
46 |
+
return new_bars
|
47 |
+
|
48 |
+
def split_patches(self, abc_text, patch_size=PATCH_SIZE, generate_last=False):
|
49 |
+
if not generate_last and len(abc_text) % patch_size != 0:
|
50 |
+
abc_text += chr(self.eos_token_id)
|
51 |
+
patches = [abc_text[i : i + patch_size] for i in range(0, len(abc_text), patch_size)]
|
52 |
+
return patches
|
53 |
+
|
54 |
+
def patch2chars(self, patch):
|
55 |
+
"""
|
56 |
+
Convert a patch into a bar.
|
57 |
+
"""
|
58 |
+
bytes = ''
|
59 |
+
for idx in patch:
|
60 |
+
if idx == self.eos_token_id:
|
61 |
+
break
|
62 |
+
if idx < self.eos_token_id:
|
63 |
+
pass
|
64 |
+
bytes += chr(idx)
|
65 |
+
return bytes
|
66 |
+
|
67 |
+
|
68 |
+
def patchilize_metadata(self, metadata_lines):
|
69 |
+
|
70 |
+
metadata_patches = []
|
71 |
+
for line in metadata_lines:
|
72 |
+
metadata_patches += self.split_patches(line)
|
73 |
+
|
74 |
+
return metadata_patches
|
75 |
+
|
76 |
+
def patchilize_tunebody(self, tunebody_lines, encode_mode='train'):
|
77 |
+
|
78 |
+
tunebody_patches = []
|
79 |
+
bars = self.split_bars(tunebody_lines)
|
80 |
+
if encode_mode == 'train':
|
81 |
+
for bar in bars:
|
82 |
+
tunebody_patches += self.split_patches(bar)
|
83 |
+
elif encode_mode == 'generate':
|
84 |
+
for bar in bars[:-1]:
|
85 |
+
tunebody_patches += self.split_patches(bar)
|
86 |
+
tunebody_patches += self.split_patches(bars[-1], generate_last=True)
|
87 |
+
|
88 |
+
return tunebody_patches
|
89 |
+
|
90 |
+
def encode_train(self, abc_text, patch_length=PATCH_LENGTH, patch_size=PATCH_SIZE, add_special_patches=True, cut=True):
|
91 |
+
|
92 |
+
lines = abc_text.split('\n')
|
93 |
+
lines = list(filter(None, lines))
|
94 |
+
lines = [line + '\n' for line in lines]
|
95 |
+
|
96 |
+
tunebody_index = -1
|
97 |
+
for i, line in enumerate(lines):
|
98 |
+
if '[V:' in line:
|
99 |
+
tunebody_index = i
|
100 |
+
break
|
101 |
+
|
102 |
+
metadata_lines = lines[ : tunebody_index]
|
103 |
+
tunebody_lines = lines[tunebody_index : ]
|
104 |
+
|
105 |
+
if self.stream:
|
106 |
+
tunebody_lines = ['[r:' + str(line_index) + '/' + str(len(tunebody_lines) - line_index - 1) + ']' + line for line_index, line in
|
107 |
+
enumerate(tunebody_lines)]
|
108 |
+
|
109 |
+
metadata_patches = self.patchilize_metadata(metadata_lines)
|
110 |
+
tunebody_patches = self.patchilize_tunebody(tunebody_lines, encode_mode='train')
|
111 |
+
|
112 |
+
if add_special_patches:
|
113 |
+
bos_patch = chr(self.bos_token_id) * (patch_size - 1) + chr(self.eos_token_id)
|
114 |
+
eos_patch = chr(self.bos_token_id) + chr(self.eos_token_id) * (patch_size - 1)
|
115 |
+
|
116 |
+
metadata_patches = [bos_patch] + metadata_patches
|
117 |
+
tunebody_patches = tunebody_patches + [eos_patch]
|
118 |
+
|
119 |
+
if self.stream:
|
120 |
+
if len(metadata_patches) + len(tunebody_patches) > patch_length:
|
121 |
+
available_cut_indexes = [0] + [index + 1 for index, patch in enumerate(tunebody_patches) if '\n' in patch]
|
122 |
+
line_index_for_cut_index = list(range(len(available_cut_indexes)))
|
123 |
+
end_index = len(metadata_patches) + len(tunebody_patches) - patch_length
|
124 |
+
biggest_index = bisect.bisect_left(available_cut_indexes, end_index)
|
125 |
+
available_cut_indexes = available_cut_indexes[:biggest_index + 1]
|
126 |
+
|
127 |
+
if len(available_cut_indexes) == 1:
|
128 |
+
choices = ['head']
|
129 |
+
elif len(available_cut_indexes) == 2:
|
130 |
+
choices = ['head', 'tail']
|
131 |
+
else:
|
132 |
+
choices = ['head', 'tail', 'middle']
|
133 |
+
choice = random.choice(choices)
|
134 |
+
if choice == 'head':
|
135 |
+
patches = metadata_patches + tunebody_patches[0:]
|
136 |
+
else:
|
137 |
+
if choice == 'tail':
|
138 |
+
cut_index = len(available_cut_indexes) - 1
|
139 |
+
else:
|
140 |
+
cut_index = random.choice(range(1, len(available_cut_indexes) - 1))
|
141 |
+
|
142 |
+
line_index = line_index_for_cut_index[cut_index]
|
143 |
+
stream_tunebody_lines = tunebody_lines[line_index : ]
|
144 |
+
|
145 |
+
stream_tunebody_patches = self.patchilize_tunebody(stream_tunebody_lines, encode_mode='train')
|
146 |
+
if add_special_patches:
|
147 |
+
stream_tunebody_patches = stream_tunebody_patches + [eos_patch]
|
148 |
+
patches = metadata_patches + stream_tunebody_patches
|
149 |
+
else:
|
150 |
+
patches = metadata_patches + tunebody_patches
|
151 |
+
else:
|
152 |
+
patches = metadata_patches + tunebody_patches
|
153 |
+
|
154 |
+
if cut:
|
155 |
+
patches = patches[ : patch_length]
|
156 |
+
else:
|
157 |
+
pass
|
158 |
+
|
159 |
+
# encode to ids
|
160 |
+
id_patches = []
|
161 |
+
for patch in patches:
|
162 |
+
id_patch = [ord(c) for c in patch] + [self.special_token_id] * (patch_size - len(patch))
|
163 |
+
id_patches.append(id_patch)
|
164 |
+
|
165 |
+
return id_patches
|
166 |
+
|
167 |
+
def encode_generate(self, abc_code, patch_length=PATCH_LENGTH, patch_size=PATCH_SIZE, add_special_patches=True):
|
168 |
+
|
169 |
+
lines = abc_code.split('\n')
|
170 |
+
lines = list(filter(None, lines))
|
171 |
+
|
172 |
+
tunebody_index = None
|
173 |
+
for i, line in enumerate(lines):
|
174 |
+
if line.startswith('[V:') or line.startswith('[r:'):
|
175 |
+
tunebody_index = i
|
176 |
+
break
|
177 |
+
|
178 |
+
metadata_lines = lines[ : tunebody_index]
|
179 |
+
tunebody_lines = lines[tunebody_index : ]
|
180 |
+
|
181 |
+
metadata_lines = [line + '\n' for line in metadata_lines]
|
182 |
+
if self.stream:
|
183 |
+
if not abc_code.endswith('\n'):
|
184 |
+
tunebody_lines = [tunebody_lines[i] + '\n' for i in range(len(tunebody_lines) - 1)] + [tunebody_lines[-1]]
|
185 |
+
else:
|
186 |
+
tunebody_lines = [tunebody_lines[i] + '\n' for i in range(len(tunebody_lines))]
|
187 |
+
else:
|
188 |
+
tunebody_lines = [line + '\n' for line in tunebody_lines]
|
189 |
+
|
190 |
+
metadata_patches = self.patchilize_metadata(metadata_lines)
|
191 |
+
tunebody_patches = self.patchilize_tunebody(tunebody_lines, encode_mode='generate')
|
192 |
+
|
193 |
+
if add_special_patches:
|
194 |
+
bos_patch = chr(self.bos_token_id) * (patch_size - 1) + chr(self.eos_token_id)
|
195 |
+
|
196 |
+
metadata_patches = [bos_patch] + metadata_patches
|
197 |
+
|
198 |
+
patches = metadata_patches + tunebody_patches
|
199 |
+
patches = patches[ : patch_length]
|
200 |
+
|
201 |
+
# encode to ids
|
202 |
+
id_patches = []
|
203 |
+
for patch in patches:
|
204 |
+
if len(patch) < PATCH_SIZE and patch[-1] != chr(self.eos_token_id):
|
205 |
+
id_patch = [ord(c) for c in patch]
|
206 |
+
else:
|
207 |
+
id_patch = [ord(c) for c in patch] + [self.special_token_id] * (patch_size - len(patch))
|
208 |
+
id_patches.append(id_patch)
|
209 |
+
|
210 |
+
return id_patches
|
211 |
+
|
212 |
+
def decode(self, patches):
|
213 |
+
"""
|
214 |
+
Decode patches into music.
|
215 |
+
"""
|
216 |
+
return ''.join(self.patch2chars(patch) for patch in patches)
|
217 |
+
|
218 |
+
|
219 |
+
|
220 |
+
|
221 |
+
class PatchLevelDecoder(PreTrainedModel):
|
222 |
+
"""
|
223 |
+
A Patch-level Decoder model for generating patch features in an auto-regressive manner.
|
224 |
+
It inherits PreTrainedModel from transformers.
|
225 |
+
"""
|
226 |
+
def __init__(self, config):
|
227 |
+
super().__init__(config)
|
228 |
+
self.patch_embedding = torch.nn.Linear(PATCH_SIZE * 128, config.n_embd)
|
229 |
+
torch.nn.init.normal_(self.patch_embedding.weight, std=0.02)
|
230 |
+
self.base = GPT2Model(config)
|
231 |
+
|
232 |
+
def forward(self,
|
233 |
+
patches: torch.Tensor,
|
234 |
+
masks=None) -> torch.Tensor:
|
235 |
+
"""
|
236 |
+
The forward pass of the patch-level decoder model.
|
237 |
+
:param patches: the patches to be encoded
|
238 |
+
:param masks: the masks for the patches
|
239 |
+
:return: the encoded patches
|
240 |
+
"""
|
241 |
+
patches = torch.nn.functional.one_hot(patches, num_classes=128).to(self.dtype)
|
242 |
+
patches = patches.reshape(len(patches), -1, PATCH_SIZE * (128))
|
243 |
+
patches = self.patch_embedding(patches.to(self.device))
|
244 |
+
|
245 |
+
if masks==None:
|
246 |
+
return self.base(inputs_embeds=patches)
|
247 |
+
else:
|
248 |
+
return self.base(inputs_embeds=patches,
|
249 |
+
attention_mask=masks)
|
250 |
+
|
251 |
+
|
252 |
+
class CharLevelDecoder(PreTrainedModel):
|
253 |
+
"""
|
254 |
+
A Char-level Decoder model for generating the chars within each patch in an auto-regressive manner
|
255 |
+
based on the encoded patch features. It inherits PreTrainedModel from transformers.
|
256 |
+
"""
|
257 |
+
def __init__(self, config):
|
258 |
+
super().__init__(config)
|
259 |
+
self.special_token_id = 0
|
260 |
+
self.bos_token_id = 1
|
261 |
+
|
262 |
+
self.base = GPT2LMHeadModel(config)
|
263 |
+
|
264 |
+
def forward(self,
|
265 |
+
encoded_patches: torch.Tensor,
|
266 |
+
target_patches: torch.Tensor):
|
267 |
+
"""
|
268 |
+
The forward pass of the char-level decoder model.
|
269 |
+
:param encoded_patches: the encoded patches
|
270 |
+
:param target_patches: the target patches
|
271 |
+
:return: the output of the model
|
272 |
+
"""
|
273 |
+
# preparing the labels for model training
|
274 |
+
target_patches = torch.cat((torch.ones_like(target_patches[:,0:1])*self.bos_token_id, target_patches), dim=1)
|
275 |
+
# print('target_patches shape:', target_patches.shape)
|
276 |
+
|
277 |
+
target_masks = target_patches == self.special_token_id
|
278 |
+
labels = target_patches.clone().masked_fill_(target_masks, -100)
|
279 |
+
|
280 |
+
# masking the labels for model training
|
281 |
+
target_masks = torch.ones_like(labels)
|
282 |
+
target_masks = target_masks.masked_fill_(labels == -100, 0)
|
283 |
+
|
284 |
+
# select patches
|
285 |
+
if PATCH_SAMPLING_BATCH_SIZE!=0 and PATCH_SAMPLING_BATCH_SIZE<target_patches.shape[0]:
|
286 |
+
indices = list(range(len(target_patches)))
|
287 |
+
random.shuffle(indices)
|
288 |
+
selected_indices = sorted(indices[:PATCH_SAMPLING_BATCH_SIZE])
|
289 |
+
|
290 |
+
target_patches = target_patches[selected_indices,:]
|
291 |
+
target_masks = target_masks[selected_indices,:]
|
292 |
+
encoded_patches = encoded_patches[selected_indices,:]
|
293 |
+
|
294 |
+
# get input embeddings
|
295 |
+
inputs_embeds = torch.nn.functional.embedding(target_patches, self.base.transformer.wte.weight)
|
296 |
+
|
297 |
+
# concatenate the encoded patches with the input embeddings
|
298 |
+
inputs_embeds = torch.cat((encoded_patches.unsqueeze(1), inputs_embeds[:,1:,:]), dim=1)
|
299 |
+
|
300 |
+
output = self.base(inputs_embeds=inputs_embeds,
|
301 |
+
attention_mask=target_masks,
|
302 |
+
labels=labels)
|
303 |
+
# output_hidden_states=True=True)
|
304 |
+
|
305 |
+
return output
|
306 |
+
|
307 |
+
def generate(self,
|
308 |
+
encoded_patch: torch.Tensor, # [hidden_size]
|
309 |
+
tokens: torch.Tensor): # [1]
|
310 |
+
"""
|
311 |
+
The generate function for generating a patch based on the encoded patch and already generated tokens.
|
312 |
+
:param encoded_patch: the encoded patch
|
313 |
+
:param tokens: already generated tokens in the patch
|
314 |
+
:return: the probability distribution of next token
|
315 |
+
"""
|
316 |
+
encoded_patch = encoded_patch.reshape(1, 1, -1) # [1, 1, hidden_size]
|
317 |
+
tokens = tokens.reshape(1, -1)
|
318 |
+
|
319 |
+
# Get input embeddings
|
320 |
+
tokens = torch.nn.functional.embedding(tokens, self.base.transformer.wte.weight)
|
321 |
+
|
322 |
+
# Concatenate the encoded patch with the input embeddings
|
323 |
+
tokens = torch.cat((encoded_patch, tokens[:,1:,:]), dim=1)
|
324 |
+
|
325 |
+
# Get output from model
|
326 |
+
outputs = self.base(inputs_embeds=tokens)
|
327 |
+
|
328 |
+
# Get probabilities of next token
|
329 |
+
probs = torch.nn.functional.softmax(outputs.logits.squeeze(0)[-1], dim=-1)
|
330 |
+
|
331 |
+
return probs
|
332 |
+
|
333 |
+
class NotaGenLMHeadModel(PreTrainedModel):
|
334 |
+
"""
|
335 |
+
NotaGen is a language model with a hierarchical structure.
|
336 |
+
It includes a patch-level decoder and a char-level decoder.
|
337 |
+
The patch-level decoder is used to generate patch features in an auto-regressive manner.
|
338 |
+
The char-level decoder is used to generate the chars within each patch in an auto-regressive manner.
|
339 |
+
It inherits PreTrainedModel from transformers.
|
340 |
+
"""
|
341 |
+
def __init__(self, encoder_config, decoder_config):
|
342 |
+
super().__init__(encoder_config)
|
343 |
+
self.special_token_id = 0
|
344 |
+
self.bos_token_id = 1
|
345 |
+
self.eos_token_id = 2
|
346 |
+
self.patch_level_decoder = PatchLevelDecoder(encoder_config)
|
347 |
+
self.char_level_decoder = CharLevelDecoder(decoder_config)
|
348 |
+
|
349 |
+
def forward(self,
|
350 |
+
patches: torch.Tensor,
|
351 |
+
masks: torch.Tensor):
|
352 |
+
"""
|
353 |
+
The forward pass of the bGPT model.
|
354 |
+
:param patches: the patches to be encoded
|
355 |
+
:param masks: the masks for the patches
|
356 |
+
:return: the decoded patches
|
357 |
+
"""
|
358 |
+
patches = patches.reshape(len(patches), -1, PATCH_SIZE)
|
359 |
+
encoded_patches = self.patch_level_decoder(patches, masks)["last_hidden_state"]
|
360 |
+
|
361 |
+
left_shift_masks = masks * (masks.flip(1).cumsum(1).flip(1) > 1)
|
362 |
+
masks[:, 0] = 0
|
363 |
+
|
364 |
+
encoded_patches = encoded_patches[left_shift_masks == 1]
|
365 |
+
patches = patches[masks == 1]
|
366 |
+
|
367 |
+
return self.char_level_decoder(encoded_patches, patches)
|
368 |
+
|
369 |
+
def generate(self,
|
370 |
+
patches: torch.Tensor,
|
371 |
+
top_k=0,
|
372 |
+
top_p=1,
|
373 |
+
temperature=1.0):
|
374 |
+
"""
|
375 |
+
The generate function for generating patches based on patches.
|
376 |
+
:param patches: the patches to be encoded
|
377 |
+
:param top_k: the top k for sampling
|
378 |
+
:param top_p: the top p for sampling
|
379 |
+
:param temperature: the temperature for sampling
|
380 |
+
:return: the generated patches
|
381 |
+
"""
|
382 |
+
if patches.shape[-1] % PATCH_SIZE != 0:
|
383 |
+
tokens = patches[:,:,-(patches.shape[-1]%PATCH_SIZE):].squeeze(0, 1)
|
384 |
+
tokens = torch.cat((torch.tensor([self.bos_token_id], device=self.device), tokens), dim=-1)
|
385 |
+
patches = patches[:,:,:-(patches.shape[-1]%PATCH_SIZE)]
|
386 |
+
else:
|
387 |
+
tokens = torch.tensor([self.bos_token_id], device=self.device)
|
388 |
+
|
389 |
+
patches = patches.reshape(len(patches), -1, PATCH_SIZE) # [bs, seq, patch_size]
|
390 |
+
encoded_patches = self.patch_level_decoder(patches)["last_hidden_state"] # [bs, seq, hidden_size]
|
391 |
+
generated_patch = []
|
392 |
+
|
393 |
+
while True:
|
394 |
+
prob = self.char_level_decoder.generate(encoded_patches[0][-1], tokens).cpu().detach().numpy() # [128]
|
395 |
+
prob = top_k_sampling(prob, top_k=top_k, return_probs=True) # [128]
|
396 |
+
prob = top_p_sampling(prob, top_p=top_p, return_probs=True) # [128]
|
397 |
+
token = temperature_sampling(prob, temperature=temperature) # int
|
398 |
+
char = chr(token)
|
399 |
+
generated_patch.append(token)
|
400 |
+
|
401 |
+
if len(tokens) >= PATCH_SIZE:# or token == self.eos_token_id:
|
402 |
+
break
|
403 |
+
else:
|
404 |
+
tokens = torch.cat((tokens, torch.tensor([token], device=self.device)), dim=0)
|
405 |
+
|
406 |
+
return generated_patch
|