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add description and join us gradio interface
Browse files- build_chord_maps.py +0 -92
- extract_chords.py +0 -73
- main.py +19 -4
build_chord_maps.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import os
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import pickle
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from tqdm import tqdm
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import argparse
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--chords_folder', type=str, required=True,
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help='path to directory containing parsed chords files')
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parser.add_argument('--output_directory', type=str, required=False,
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help='path to output directory to generate code maps to, \
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if not given - chords_folder would be used', default='')
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parser.add_argument('--path_to_pre_defined_map', type=str, required=False,
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help='for evaluation purpose, use pre-defined chord-to-index map', default='')
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args = parser.parse_args()
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return args
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def get_chord_dict(chord_folder: str):
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chord_dict = {}
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distinct_chords = set()
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chord_to_index = {} # Mapping between chord and index
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index_counter = 0
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for filename in tqdm(os.listdir(chord_folder)):
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if filename.endswith(".chords"):
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idx = filename.split(".")[0]
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with open(os.path.join(chord_folder, filename), "rb") as file:
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chord_data = pickle.load(file)
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for chord, _ in chord_data:
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distinct_chords.add(chord)
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if chord not in chord_to_index:
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chord_to_index[chord] = index_counter
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index_counter += 1
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chord_dict[idx] = chord_data
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chord_to_index["UNK"] = index_counter
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return chord_dict, distinct_chords, chord_to_index
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def get_predefined_chord_to_index_map(path_to_chords_to_index_map: str):
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def inner(chord_folder: str):
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chords_to_index = pickle.load(open(path_to_chords_to_index_map, "rb"))
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distinct_chords = set(chords_to_index.keys())
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chord_dict = {}
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for filename in tqdm(os.listdir(chord_folder), desc=f'iterating: {chord_folder}'):
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if filename.endswith(".chords"):
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idx = filename.split(".")[0]
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with open(os.path.join(chord_folder, filename), "rb") as file:
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chord_data = pickle.load(file)
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chord_dict[idx] = chord_data
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return chord_dict, distinct_chords, chords_to_index
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return inner
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if __name__ == "__main__":
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'''This script processes and maps chord data from a directory of parsed chords files,
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generating two output files: a combined chord dictionary and a chord-to-index mapping.'''
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args = parse_args()
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chord_folder = args.chords_folder
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output_dir = args.output_directory
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if output_dir == '':
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output_dir = chord_folder
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func = get_chord_dict
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if args.path_to_pre_defined_map != "":
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func = get_predefined_chord_to_index_map(args.path_to_pre_defined_map)
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chord_dict, distinct_chords, chord_to_index = func(chord_folder)
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# Save the combined chord dictionary as a pickle file
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combined_filename = os.path.join(output_dir, "combined_chord_dict.pkl")
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with open(combined_filename, "wb") as file:
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pickle.dump(chord_dict, file)
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# Save the chord-to-index mapping as a pickle file
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mapping_filename = os.path.join(output_dir, "chord_to_index_mapping.pkl")
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with open(mapping_filename, "wb") as file:
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pickle.dump(chord_to_index, file)
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print("Number of distinct chords:", len(distinct_chords))
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print("Chord dictionary:", chord_to_index)
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extract_chords.py
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# Env - chords_extraction on devfair
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import pickle
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import argparse
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from chord_extractor.extractors import Chordino # type: ignore
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from chord_extractor import clear_conversion_cache, LabelledChordSequence # type: ignore
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import os
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from tqdm import tqdm
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--src_jsonl_file', type=str, required=True,
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help='abs path to .jsonl file containing list of absolute file paths seperated by new line')
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parser.add_argument('--target_output_dir', type=str, required=True,
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help='target directory to save parsed chord files to, individual files will be saved inside')
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parser.add_argument("--override", action="store_true")
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args = parser.parse_args()
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return args
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def save_to_db_cb(tgt_dir: str):
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# Every time one of the files has had chords extracted, receive the chords here
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# along with the name of the original file and then run some logic here, e.g. to
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# save the latest data to DB
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def inner(results: LabelledChordSequence):
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path = results.id.split(".wav")
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sequence = [(item.chord, item.timestamp) for item in results.sequence]
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if len(path) != 2:
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print("Something")
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print(path)
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else:
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file_idx = path[0].split("/")[-1]
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with open(f"{tgt_dir}/{file_idx}.chords", "wb") as f:
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# dump the object to the file
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pickle.dump(sequence, f)
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return inner
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if __name__ == "__main__":
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'''This script extracts chord data from a list of audio files using the Chordino extractor,
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and saves the extracted chords to individual files in a target directory.'''
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print("parsed args")
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args = parse_args()
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files_to_extract_from = list()
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with open(args.src_jsonl_file, "r") as json_file:
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for line in tqdm(json_file.readlines()):
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# fpath = json.loads(line.replace("\n", ""))['path']
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fpath = line.replace("\n", "")
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if not args.override:
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fname = fpath.split("/")[-1].replace(".wav", ".chords")
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if os.path.exists(f"{args.target_output_dir}/{fname}"):
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continue
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files_to_extract_from.append(line.replace("\n", ""))
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print(f"num files to parse: {len(files_to_extract_from)}")
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chordino = Chordino()
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# Optionally clear cache of file conversions (e.g. wav files that have been converted from midi)
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clear_conversion_cache()
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# Run bulk extraction
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res = chordino.extract_many(
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files_to_extract_from,
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callback=save_to_db_cb(args.target_output_dir),
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num_extractors=80,
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num_preprocessors=80,
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max_files_in_cache=400,
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stop_on_error=False,
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)
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main.py
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@@ -16,6 +16,17 @@ from audiocraft.models import JASCO
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import os
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from huggingface_hub import login
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hf_token = os.environ.get('HFTOKEN')
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if hf_token:
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login(token=hf_token)
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return wavs
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with gr.Blocks() as demo:
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gr.Markdown(
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with gr.Row():
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with gr.Column():
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import os
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from huggingface_hub import login
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title = """# 🙋🏻♂️Welcome to 🌟Tonic's 🎼Jasco🎶AudioCraft Demo"""
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description = """Facebook presents JASCO, a temporally controlled text-to-music generation model utilizing both symbolic and audio-based conditions. JASCO can generate high-quality music samples conditioned on global text descriptions along with fine-grained local controls. JASCO is based on the Flow Matching modeling paradigm together with a novel conditioning method, allowing for music generation controlled both locally (e.g., chords) and globally (text description)."""
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join_us = """
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## Join us:
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🌟TeamTonic🌟 is always making cool demos! Join our active builder's 🛠️community 👻
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[![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/qdfnvSPcqP)
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On 🤗Huggingface: [MultiTransformer](https://huggingface.co/MultiTransformer)
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On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [MultiTonic](https://github.com/MultiTonic/thinking-dataset)
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🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
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"""
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hf_token = os.environ.get('HFTOKEN')
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if hf_token:
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login(token=hf_token)
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return wavs
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with gr.Blocks() as demo:
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gr.Markdown(title)
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with gr.Row():
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with gr.Column():
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with gr.Group():
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gr.Markdown(description)
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with gr.Column():
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with gr.Group():
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gr.Markdown(join_us)
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with gr.Row():
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with gr.Column():
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