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007017f 681b241 c5184df 681b241 007017f cdd58eb c5184df 681b241 cdd58eb c5184df 681b241 c5184df 681b241 007017f 681b241 cdd58eb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | import logging
import numpy as np
from gradio_client import Client
import os
import json
import time
import httpx
logger = logging.getLogger(__name__)
from chord_extractor.extractors import Chordino
from chord_extractor import clear_conversion_cache, LabelledChordSequence
_CONSTANT_GAP_SECS = 2
_SEQ_EMBED_SPACE = 'ohollo/chord-seq-embed'
_POST_PROCESS_CHORD_LEN_RATIO = 0.7
_MAX_RETRIES = 3
_RETRY_DELAY_SECS = 2
def _create_client():
for attempt in range(_MAX_RETRIES):
try:
return Client(_SEQ_EMBED_SPACE)
except httpx.ReadTimeout:
if attempt < _MAX_RETRIES - 1:
time.sleep(_RETRY_DELAY_SECS)
else:
raise
_client = _create_client()
def _call_embedding_service(chords_w_timestamps):
logger.info(chords_w_timestamps)
result = _client.predict(json.dumps(chords_w_timestamps), api_name="/predict")
return json.loads(result)
def get_embeddings_from_chord_sequences(chord_sequences: list[list[str]], constant_gap_secs: float = _CONSTANT_GAP_SECS) -> np.ndarray:
"""
Converts chord sequences into its corresponding embeddings.
:param chord_sequence: List of chords representing the chord sequence.
:return: 2-d numpy array of embeddings per chord sequence.
"""
chords_w_timestamps = [
{'label': chord_sequence, 'timestamp': [i* constant_gap_secs for i, _ in enumerate(chord_sequence)]}
for chord_sequence in chord_sequences
]
return np.array(_call_embedding_service(chords_w_timestamps)['embeddings'])
def get_embedding_from_filepaths(file_paths: list[str]) -> tuple[np.ndarray, list[int]]:
"""
Reads chord sequences from a given filepath and converts them into embeddings.
:param file_paths: List of paths to the audio files. Can be anything supported by chord-extractor - .mid, .wav, .mp3, .ogg
:return: 2-d numpy array of embeddings per chord sequence.
"""
chords_w_timestamps = []
chord_lengths = []
chordino = Chordino()
for file_path in file_paths:
if not os.path.isfile(file_path):
raise FileNotFoundError(f"File not found: {file_path}")
conversion_file_path = chordino.preprocess(file_path)
chords = chordino.extract(conversion_file_path if conversion_file_path else file_path)
chords_w_timestamps.append({
'label': [chord.chord for chord in chords],
'timestamp': [chord.timestamp for chord in chords]
})
chord_lengths.append(int(len(chords) * _POST_PROCESS_CHORD_LEN_RATIO))
return np.array(_call_embedding_service(chords_w_timestamps)['embeddings']), chord_lengths |