From 98394cb5883afaebcbeab594e24a11b2c74dfc70 Mon Sep 17 00:00:00 2001 From: Nintorac Dev Date: Tue, 3 Sep 2024 18:31:12 +1000 Subject: [PATCH 01/11] [chore] update deps to remove gpu dependency --- Makefile | 8 + poetry.lock | 5207 +++++++++++++++++++++++++++--------------------- pyproject.toml | 30 +- 3 files changed, 2917 insertions(+), 2328 deletions(-) create mode 100644 Makefile diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..00327e3 --- /dev/null +++ b/Makefile @@ -0,0 +1,8 @@ + +.PHONY: install_data_deps install_train_deps + + +install_data_deps: + poetry install --only vis --only data + + diff --git a/poetry.lock b/poetry.lock index 51fc1eb..6bc3f95 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,14 +1,14 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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notebooks/validate_voice_cleaning.py create mode 100644 s4_dx7/lib/midi.py create mode 100644 s4_dx7/utils.py create mode 100644 s4_dx7_dbt/models/dx7_patch/clean_dx7_voices.sql create mode 100644 s4_dx7_dbt/models/dx7_patch/distinct_dx7_voices.sql rename s4_dx7_dbt/models/{ => dx7_patch}/dx7_voices.sql (100%) create mode 100644 s4_dx7_dbt/models/dx7_patch/metadata_dx7_voices.sql create mode 100644 s4_dx7_dbt/models/dx7_patch/schema.yml diff --git a/notebooks/validate_voice_cleaning.py b/notebooks/validate_voice_cleaning.py new file mode 100644 index 0000000..56916a1 --- /dev/null +++ b/notebooks/validate_voice_cleaning.py @@ -0,0 +1,43 @@ +#%% [markdown] +# you should run dexed alongside this notebook, +# it will send out dx7 patches to the midi bus +# if dexed is running it should pick it up and load the patch +# play around with it and load the next patch by providing empty input +# if you want to stop type anything into the input +#%% +import json +from pathlib import Path +import duckdb +import mido +from s4_dx7.lib.dx7 import DX7Single, clean_voice +from s4_dx7.utils import get_duckdb_connection + +#%% +duck = get_duckdb_connection('data/dev.duckdb') +# duck.execute("attach 'data/audio.db' as audio") +#%% +# voices = duck.query('select * from dx7_voices') +voices = duck.query('select * from dx7_voices').limit(100) +print(mido.get_input_names()) +## change if input name is different +port = mido.open_output('Midi Through:Midi Through Port-0 14:0') +for voice in voices.to_df().itertuples(): + print(voice.path) + print(voice.index) + port.send(mido.Message.from_bytes(voice.bytes)) + i = input() + if i: break +#%% +voices = duck.query("select * from metadata_dx7_voices order by random() limit 100") +voices +# %% +mido.get_input_names() +port = mido.open_output('Midi Through:Midi Through Port-0 14:0') +for voice in voices.to_df().itertuples(): + print(voice.voice_id) + print(voice.metadata) + port.send(mido.Message.from_bytes(voice.bytes)) + i = input() + if i: break +# %% +# %% diff --git a/s4_dx7/lib/dx7.py b/s4_dx7/lib/dx7.py index e7c304f..fb19e71 100644 --- a/s4_dx7/lib/dx7.py +++ b/s4_dx7/lib/dx7.py @@ -1,7 +1,14 @@ +# info from https://homepages.abdn.ac.uk/d.j.benson/pages/dx7/sysex-format.txt +# this file started as a hack and only got worse from there +# TODO rewrite with ux in mind +from itertools import zip_longest from pathlib import Path +from tempfile import NamedTemporaryFile import bitstruct import mido +from s4_dx7.lib.midi import sysex_bytes + def take(take_from, n): for _ in range(n): @@ -9,6 +16,7 @@ def take(take_from, n): N_OSC = 6 N_VOICES = 32 +N_NAME_CHARS = 10 def checksum(data): return (128-sum(data)&127)%128 @@ -330,10 +338,28 @@ def struct(): @staticmethod - def to_syx(voices): + def single_syx_message(voices) -> mido.Message: + if len(voices)!=1: + raise ValueError("only a single voice should be provided") + voice = voices[0] + voices_bytes = bytes() + voices_bytes = DX7Single.struct().pack(dict(zip(DX7Single.keys(), voice))) + + patch_checksum = [checksum(voices_bytes)] + + data = bytes(DX7Single.HEADER) \ + + voices_bytes \ + + bytes(patch_checksum) - assert len(voices)==1 + + return mido.Message('sysex', data=data) + + @staticmethod + def multi_to_single_syx_message(voices) -> mido.Message: + + if len(voices)!=1: + raise ValueError("only a single voice should be provided") voice = voices[0] voices_bytes = bytes() voices_bytes = DX7Single.struct().pack(dict(zip(VOICE_KEYS, voice))) @@ -347,6 +373,8 @@ def to_syx(voices): return mido.Message('sysex', data=data) +# DX7Single.GENERAL_KEYS=GENERAL_KEYS +# DX7Single.OSC_KEYS=OSC_KEYS def consume_syx(path): @@ -394,8 +422,28 @@ def dx7_bulk_pack(voices): return mido.Message('sysex', data=data) - -if __name__=="__main__": - print(VOICE_KEYS) - - # print(DX7Single.to_syx(n) \ No newline at end of file +def clean_voice(voice: bytes, name_str='NINTORAC', pad='_') -> bytes: + """ + Normalises all fields that have no effect on the synthesis + TODO: python tests to ensure that the audio signal is the same + before and after cleaning + """ + + if len(name_str) > N_NAME_CHARS: + raise ValueError('patch name must be at most 10 chars') + voice_sans_header = bytes(voice[6:-2]) + voice_dict= DX7Single.struct().unpack(voice_sans_header) + for i, c in zip_longest(range(N_NAME_CHARS), name_str): + c = c or pad + voice_dict[f'NAME CHAR {i+1}'] = int.from_bytes(c.encode('ascii')) + + sysex_bytes_voice = sysex_bytes(DX7Single.single_syx_message([[voice_dict[key] for key in DX7Single.keys()]])) + + return sysex_bytes_voice + +def extract_voices(cart_bytes: bytes): + with NamedTemporaryFile('r+b') as f: + f.write(cart_bytes) + voices = consume_syx(f.name) + to_sysex = lambda x: sysex_bytes(DX7Single.multi_to_single_syx_message(x)) + return [to_sysex([x.values()]) for x in voices if x is not None] diff --git a/s4_dx7/lib/midi.py b/s4_dx7/lib/midi.py new file mode 100644 index 0000000..7b57b72 --- /dev/null +++ b/s4_dx7/lib/midi.py @@ -0,0 +1,9 @@ +import mido +from tempfile import NamedTemporaryFile + + +def sysex_bytes(sysex: mido.Message): + with NamedTemporaryFile('rb') as f: + mido.write_syx_file(f.name, [sysex]) + sysex_bytes = f.read() + return sysex_bytes \ No newline at end of file diff --git a/s4_dx7/udf/__init__.py b/s4_dx7/udf/__init__.py index c5869f5..b49fb7b 100644 --- a/s4_dx7/udf/__init__.py +++ b/s4_dx7/udf/__init__.py @@ -2,11 +2,12 @@ from duckdb import DuckDBPyConnection from dbt.adapters.duckdb.plugins import BasePlugin -from s4_dx7.udf.dx7 import extract_voices_udf +from s4_dx7.udf.dx7 import clean_voice_udf, extract_voices_udf from s4_dx7.udf.render import render_midi_udf class Plugin(BasePlugin): def configure_connection(self, conn: DuckDBPyConnection): conn.create_function("render_midi", render_midi_udf, type="arrow") conn.create_function("extract_voices", extract_voices_udf, type="arrow") + conn.create_function("clean_voice", clean_voice_udf, type="arrow") \ No newline at end of file diff --git a/s4_dx7/udf/dx7.py b/s4_dx7/udf/dx7.py index 02d5fa6..db8fa79 100644 --- a/s4_dx7/udf/dx7.py +++ b/s4_dx7/udf/dx7.py @@ -1,27 +1,22 @@ +from typing import List import duckdb -from duckdb.typing import BLOB, DuckDBPyType -import mido -from s4_dx7.lib.dx7 import DX7Single, consume_syx -from tempfile import NamedTemporaryFile +from duckdb.typing import BLOB, DuckDBPyType, BIGINT +from s4_dx7.lib.dx7 import clean_voice +from s4_dx7.lib.dx7 import extract_voices -VOICES_TYPE = DuckDBPyType(list[{'bytes': bytes, 'index':int}]) - -def sysex_bytes(sysex): - with NamedTemporaryFile('rb') as f: - mido.write_syx_file(f.name, [sysex]) - sysex_bytes = f.read() - return sysex_bytes - -def extract_voices(cart_bytes: bytes): - with NamedTemporaryFile('r+b') as f: - f.write(cart_bytes) - return [sysex_bytes(DX7Single.to_syx([x.values()])) for x in consume_syx(f.name) if x is not None] +VOICES_TYPE = DuckDBPyType(list[{'bytes': bytes, 'index':int}]) def extract_voices_udf(cart_bytes: BLOB)->VOICES_TYPE: cart_voices = [] for row in cart_bytes.to_pandas(): extracted = extract_voices(row) cart_voices.append([{'index':i, 'bytes': bytes(sysex_data)} for i, sysex_data in enumerate(extracted)]) - return cart_voices \ No newline at end of file + return cart_voices + +def clean_voice_udf(voice_bytes: BLOB) -> BLOB: + voices_df = voice_bytes.to_pandas() + return voices_df.map(clean_voice) + + diff --git a/s4_dx7/utils.py b/s4_dx7/utils.py new file mode 100644 index 0000000..45b65b1 --- /dev/null +++ b/s4_dx7/utils.py @@ -0,0 +1,15 @@ +import duckdb + +from s4_dx7.udf import Plugin + + +def get_duckdb_connection(path=':memory:'): + """ + inits the duckdb connection according to the dbt setup + + TODO: this is a total hack make this use + the actual dbt setup pathway + """ + conn = duckdb.connect(path) + Plugin.configure_connection(None, conn) + return conn \ No newline at end of file diff --git a/s4_dx7_dbt/dbt_project.yml b/s4_dx7_dbt/dbt_project.yml index 12bfc4b..3e45147 100644 --- a/s4_dx7_dbt/dbt_project.yml +++ b/s4_dx7_dbt/dbt_project.yml @@ -39,6 +39,7 @@ models: melodies: # +materialized:`` incremental +database: audio + # TODO: find a nice way to send some things to different database. dx7_voices: +database: audio dx7_cartridges: diff --git a/s4_dx7_dbt/models/dx7_cartridges.py b/s4_dx7_dbt/models/dx7_cartridges.py index 6444260..cb3368b 100644 --- a/s4_dx7_dbt/models/dx7_cartridges.py +++ b/s4_dx7_dbt/models/dx7_cartridges.py @@ -1,3 +1,4 @@ +from itertools import chain from pathlib import Path import pandas as pd import pyarrow as pa @@ -6,7 +7,12 @@ def model(dbt, session): carts = [] - for path in Path(CART_SOURCE).glob('**/*.syx'): + sources = [ + Path(CART_SOURCE).glob('**/*.syx'), + Path(CART_SOURCE).glob('**/*.SYX'), + ] + for path in chain(*sources): + if path.is_dir(): continue carts.append({ 'path': path.relative_to(CART_SOURCE).as_posix(), 'bytes': bytes(path.read_bytes()) diff --git a/s4_dx7_dbt/models/dx7_patch/clean_dx7_voices.sql b/s4_dx7_dbt/models/dx7_patch/clean_dx7_voices.sql new file mode 100644 index 0000000..b2fe2ca --- /dev/null +++ b/s4_dx7_dbt/models/dx7_patch/clean_dx7_voices.sql @@ -0,0 +1,11 @@ +with clean_voices as ( + select + path + , index + , clean_voice(bytes) bytes + from {{ ref('dx7_voices') }} +) +select + md5(to_base64(bytes)) voice_id + , * +from clean_voices \ No newline at end of file diff --git a/s4_dx7_dbt/models/dx7_patch/distinct_dx7_voices.sql b/s4_dx7_dbt/models/dx7_patch/distinct_dx7_voices.sql new file mode 100644 index 0000000..5bab559 --- /dev/null +++ b/s4_dx7_dbt/models/dx7_patch/distinct_dx7_voices.sql @@ -0,0 +1,2 @@ +select DISTINCT voice_id, bytes +from {{ ref('clean_dx7_voices') }} \ No newline at end of file diff --git a/s4_dx7_dbt/models/dx7_voices.sql b/s4_dx7_dbt/models/dx7_patch/dx7_voices.sql similarity index 100% rename from s4_dx7_dbt/models/dx7_voices.sql rename to s4_dx7_dbt/models/dx7_patch/dx7_voices.sql diff --git a/s4_dx7_dbt/models/dx7_patch/metadata_dx7_voices.sql b/s4_dx7_dbt/models/dx7_patch/metadata_dx7_voices.sql new file mode 100644 index 0000000..06c110f --- /dev/null +++ b/s4_dx7_dbt/models/dx7_patch/metadata_dx7_voices.sql @@ -0,0 +1,11 @@ +with voices as ( +SELECT c.voice_id, c.bytes, path, index from {{ ref('distinct_dx7_voices') }} d +join {{ ref('clean_dx7_voices') }} c +on c.voice_id=d.voice_id +) +select + voice_id, + bytes, + list({path: path, index: index}) metadata +from voices +GROUP BY voice_id, bytes \ No newline at end of file diff --git a/s4_dx7_dbt/models/dx7_patch/schema.yml b/s4_dx7_dbt/models/dx7_patch/schema.yml new file mode 100644 index 0000000..47894f4 --- /dev/null +++ b/s4_dx7_dbt/models/dx7_patch/schema.yml @@ -0,0 +1,6 @@ +version: 2 + +models: +- name: metadata_dx7_voices + config: + materialized: view \ No newline at end of file diff --git a/s4_dx7_dbt/profiles.yml b/s4_dx7_dbt/profiles.yml index 6bd2908..c0a8690 100644 --- a/s4_dx7_dbt/profiles.yml +++ b/s4_dx7_dbt/profiles.yml @@ -11,4 +11,15 @@ s4_dx7_dbt: - /home/athena/nintorac/s4_dx7/s4_dx7 plugins: - module: s4_dx7.udf + staging: + type: duckdb + path: data/staging.duckdb + threads: 1 + attach: + - path: data/audio.db + type: sqlite + module_paths: + - /home/athena/nintorac/s4_dx7/s4_dx7 + plugins: + - module: s4_dx7.udf target: dev From 5710fce95fa3e9298bb8476be61b32c95d0f335c Mon Sep 17 00:00:00 2001 From: Nintorac Dev Date: Tue, 3 Sep 2024 20:55:41 +1000 Subject: [PATCH 03/11] [feat] add fsk encoding lib methods --- notebooks/fsk_patch_visualisations.py | 45 +++++++++++++++++++++ s4_dx7/lib/audio/__init__.py | 0 s4_dx7/lib/audio/digital.py | 58 +++++++++++++++++++++++++++ 3 files changed, 103 insertions(+) create mode 100644 notebooks/fsk_patch_visualisations.py create mode 100644 s4_dx7/lib/audio/__init__.py create mode 100644 s4_dx7/lib/audio/digital.py diff --git a/notebooks/fsk_patch_visualisations.py b/notebooks/fsk_patch_visualisations.py new file mode 100644 index 0000000..801f045 --- /dev/null +++ b/notebooks/fsk_patch_visualisations.py @@ -0,0 +1,45 @@ +#%% +from s4_dx7.lib.audio.digital import fsk_encode_bytes_batch +import matplotlib.pyplot as plt +import numpy as np +import scipy.signal as s +from matplotlib import pyplot as plt +from s4_dx7.lib.audio.digital import fsk_encode_syx + +from s4_dx7.utils import get_duckdb_connection +duck = get_duckdb_connection('data/dev.duckdb') +#%% +signal = fsk_encode_bytes_batch([b'lB', b'ab'], sampling_rate=8000) +sampling_rate = 16000 +baud_rate = sampling_rate//8 +t = np.arange(0, signal.shape[1] / sampling_rate, 1.0 / sampling_rate) +plt.plot(t, signal[0].numpy()+1) +plt.plot(t, signal[1].numpy()-1) +signal = fsk_encode_syx([b'ab', b'cd'], sampling_rate=sampling_rate) +t = np.arange(0, signal.shape[1] / sampling_rate, 1.0 / sampling_rate) +plt.plot(t, signal[0].numpy()+1) +plt.plot(t, signal[1].numpy()-1) +# %% +voices = duck.query("select * from metadata_dx7_voices order by random() limit 2").to_df() +signal = fsk_encode_syx(voices.bytes, sampling_rate=sampling_rate, baud_rate=baud_rate) +print(signal.shape) +#%% +truncate = 200 +skip = 500 +t = np.arange(0, signal.shape[1] / sampling_rate, 1.0 / sampling_rate)[skip:skip+truncate] +dt = 1/baud_rate*sampling_rate # your desired spacing +x_locations = np.arange(skip, skip+truncate, dt)/sampling_rate + +for x in x_locations: + plt.axvline(x=x, color='k', linestyle='--') + +plt.plot(t, signal[0, skip:skip+truncate].numpy()+1) +plt.plot(t, signal[1, skip:skip+truncate].numpy()-1) +#%% + +for x in x_locations: + plt.axvline(x=x, color='k', linestyle='--') +f, t, Zxx = s.stft(signal[0, skip:skip+truncate], nfft=128, fs=sampling_rate, nperseg=dt//2, noverlap=dt//4, padded=True) +plt.pcolormesh(t+skip/sampling_rate, f, np.abs(Zxx)) +# %% + diff --git a/s4_dx7/lib/audio/__init__.py b/s4_dx7/lib/audio/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/s4_dx7/lib/audio/digital.py b/s4_dx7/lib/audio/digital.py new file mode 100644 index 0000000..9a76b19 --- /dev/null +++ b/s4_dx7/lib/audio/digital.py @@ -0,0 +1,58 @@ +from typing import List +import torch +from bitarray import bitarray +from torchaudio.transforms import Fade + +NIBBLE_BITS = 4 + +def fsk_encode_bytes_batch( + encode_bytes: List[bytes], + sampling_rate = 44100, + baud_rate = 300, +): + """ + Generates the frequency codebook automatically based on the sampling rate + + taken from https://dsp.stackexchange.com/questions/80768/fsk-modulation-with-python + """ + # TODO: the author suggests some optimisations around the phi calcs + # allow for mfsk + + nyquist_limit = sampling_rate//2 + max_min_offset = (nyquist_limit)//10 + codebook = torch.linspace(max_min_offset, nyquist_limit-max_min_offset, 2) + samples_per_bit = int(1.0 / baud_rate * sampling_rate) + # tones representing bits, dummy data (0,1) + arrs = [] + for item in encode_bytes: + bits = bitarray() + bits.frombytes(item) + bits_in_tones = [codebook[int(bit)] for bit in bits] + arrs.append(torch.tensor(bits_in_tones)) + bit_arr = torch.stack(arrs) + symbols_freqs = bit_arr.repeat_interleave(samples_per_bit, -1) + + + # New lines here demonstrating continuous phase FSK (CPFSK) + delta_phi = symbols_freqs * torch.pi / (sampling_rate / 2.0) + phi = torch.cumsum(delta_phi, dim=1) + signal = torch.sin(phi) + return signal + +def fsk_encode_syx( + syx: List[bytearray], + sampling_rate=44100, + baud_rate=300 +): + """ + Encodes a syx message into an FSK signal + adds some padding and a fade out to the end to avoid signal discontinuities + """ + fade = Fade(fade_out_len=int(NIBBLE_BITS/baud_rate*sampling_rate)) # fade the final nibble of the padded encoding + pad_bytes = b'0' + padded_bytes = [signal + pad_bytes for signal in syx] + signal = fsk_encode_bytes_batch(padded_bytes, sampling_rate, baud_rate) + + signal = fade(signal) + return signal + From b3edb36828f546d136a80e6c09529272bed5b569 Mon Sep 17 00:00:00 2001 From: Nintorac Dev Date: Tue, 3 Sep 2024 21:06:57 +1000 Subject: [PATCH 04/11] [chore] refactor data module stuff --- dataloader_burn_test.py | 4 +- notebooks/data_flow.py | 10 +- .../single_voice2voice.py} | 4 +- notebooks/fir_inference.py | 10 +- s4/src/dataloaders/audio.py | 4 +- .../data/multi_voice_to_voice.py} | 2 +- .../lightning/data/single_voice_to_voice.py | 105 ++++++++++++++++++ 7 files changed, 122 insertions(+), 17 deletions(-) rename notebooks/{dataloader.py => dataloaders/single_voice2voice.py} (88%) rename s4_dx7/{lib/data/audio_data_module.py => lightning/data/multi_voice_to_voice.py} (99%) create mode 100644 s4_dx7/lightning/data/single_voice_to_voice.py diff --git a/dataloader_burn_test.py b/dataloader_burn_test.py index b220deb..ed7e93b 100644 --- a/dataloader_burn_test.py +++ b/dataloader_burn_test.py @@ -14,13 +14,13 @@ logging.getLogger("ray.data._internal.execution.streaming_executor").setLevel(logging.ERROR) # logger.setLevel(logging.ERROR) -from s4_dx7.lib.data.audio_data_module import AudioDataModule +from s4_dx7.lightning.data.single_voice_to_voice import SingleVoice2VoiceDataModule import ray ray.init(log_to_driver=False) bit_rate=8 sr=8000 sample_size=8 -data_module = AudioDataModule(bit_rate=bit_rate, sr=sr, limit=6400) +data_module = SingleVoice2VoiceDataModule(bit_rate=bit_rate, sr=sr, limit=6400) # Iterate over each batch (assuming a single batch here for simplicity) for i in tqdm(count()): c=count() diff --git a/notebooks/data_flow.py b/notebooks/data_flow.py index 822f154..3f839d7 100644 --- a/notebooks/data_flow.py +++ b/notebooks/data_flow.py @@ -8,7 +8,7 @@ import duckdb from matplotlib import pyplot as plt import torch -from s4_dx7.lib.data.audio_data_module import AudioDataModule +from s4_dx7.lightning.data.single_voice_to_voice import SingleVoice2VoiceDataModule from s4_dx7.lib.render import render_batch, to_midi from s4_dx7.lib.s4.generate import load_experiment from s4_dx7.lib.visualistaion.audio import create_melspec_figure, waveform_segment_figure, render_piano_roll, render_voice @@ -116,7 +116,7 @@ def wrapper(*args, **kwargs): load_data=False, experiment_root=f"{model_path}/s4-dx7-vc-fir" ) -source_patch, target_patch = AudioDataModule.get_voices() +source_patch, target_patch = SingleVoice2VoiceDataModule.get_voices() # %% import base64 from mido import MidiFile, MidiTrack, Message @@ -156,7 +156,7 @@ def normalize_signal(signal, bit_rate): def greedy_decode(signal): return signal.argmax(-1) def corrupt_signal(signal): - return AudioDataModule.clean_signal(signal, 7, 8) + return SingleVoice2VoiceDataModule.clean_signal(signal, 7, 8) return torch.clamp(signal + int(signal.float().mean()), 0, 2**(8-1)) # return torch.clamp(signal, 0, 2**8-1) # Function to create Mel Spectrogram plot @@ -174,8 +174,8 @@ def s4_dx7_vc_fir_00(*args, **kwargs): source_signal = render_voice(ar['notes'], source_patch, config.dataset) broken_source_signal = corrupt_signal(source_signal)[..., :-1] broken_target_signal = corrupt_signal(target_signal)[..., 1:] -source_signal = AudioDataModule.clean_signal(source_signal, 8, 8)[..., :-1] -target_signal = AudioDataModule.clean_signal(target_signal, 8, 8)[..., 1:] +source_signal = SingleVoice2VoiceDataModule.clean_signal(source_signal, 8, 8)[..., :-1] +target_signal = SingleVoice2VoiceDataModule.clean_signal(target_signal, 8, 8)[..., 1:] with torch.no_grad(): generated_signal_logits = s4_dx7_vc_fir_00((source_signal, target_signal.unsqueeze(-1)))[0] diff --git a/notebooks/dataloader.py b/notebooks/dataloaders/single_voice2voice.py similarity index 88% rename from notebooks/dataloader.py rename to notebooks/dataloaders/single_voice2voice.py index 6320fe0..fcac880 100644 --- a/notebooks/dataloader.py +++ b/notebooks/dataloaders/single_voice2voice.py @@ -6,7 +6,7 @@ from IPython.display import Audio, display import numpy as np import pandas as pd -from s4_dx7.lib.data.audio_data_module import AudioDataModule +from s4_dx7.lightning.data.single_voice_to_voice import SingleVoice2VoiceDataModule from s4_dx7.lib.visualistaion.audio import waveform_segment_figure from s4_dx7.notebook.mel_spec_audio import plot_melspectrogram_and_play_button #%% @@ -14,7 +14,7 @@ bit_rate=16 sr=8000 sample_size=15 -data_module = AudioDataModule(bit_rate=bit_rate, sr=sr, limit=sample_size) +data_module = SingleVoice2VoiceDataModule(bit_rate=bit_rate, sr=sr, limit=sample_size) # Iterate over each batch (assuming a single batch here for simplicity) for batch in data_module.get_train_dataloader(sample_size): batch_size = batch['x'].shape[0] diff --git a/notebooks/fir_inference.py b/notebooks/fir_inference.py index 3f796f5..7966afb 100644 --- a/notebooks/fir_inference.py +++ b/notebooks/fir_inference.py @@ -10,7 +10,7 @@ from IPython.core.display import HTML, display from matplotlib import pyplot as plt import torch -from s4_dx7.lib.data.audio_data_module import AudioDataModule +from s4_dx7.lightning.data.single_voice_to_voice import SingleVoice2VoiceDataModule from s4_dx7.lib.render import render_batch, to_midi from s4_dx7.lib.s4.generate import load_experiment from s4_dx7.lib.visualistaion.audio import create_melspec_figure, waveform_segment_figure, render_piano_roll, render_voice @@ -40,7 +40,7 @@ experiment_root=f"{model_path}" ) model = model.cuda() -source_patch, target_patch = AudioDataModule.get_voices() +source_patch, target_patch = SingleVoice2VoiceDataModule.get_voices() # %% import base64 from mido import MidiFile, MidiTrack, Message @@ -80,7 +80,7 @@ def normalize_signal(signal, bit_rate): def greedy_decode(signal): return signal.argmax(-1) def corrupt_signal(signal): - return AudioDataModule.clean_signal(signal, 7, 8) + return SingleVoice2VoiceDataModule.clean_signal(signal, 7, 8) return torch.clamp(signal + int(signal.float().mean()), 0, 2**(8-1)) # return torch.clamp(signal, 0, 2**8-1) # Function to create Mel Spectrogram plot @@ -114,8 +114,8 @@ def html_encode_figure(fig): target_signal = render_voice(ar['notes'], target_patch, config.dataset) source_signal = render_voice(ar['notes'], source_patch, config.dataset) -source_signal = AudioDataModule.clean_signal(source_signal, 8, 8)[..., 1:] -target_signal = AudioDataModule.clean_signal(target_signal, 8, 8)[..., 1:] +source_signal = SingleVoice2VoiceDataModule.clean_signal(source_signal, 8, 8)[..., 1:] +target_signal = SingleVoice2VoiceDataModule.clean_signal(target_signal, 8, 8)[..., 1:] with torch.no_grad(): generated_signal_logits = s4_dx7_vc_fir_00((source_signal.cuda(), target_signal.cuda().unsqueeze(-1)))[0] diff --git a/s4/src/dataloaders/audio.py b/s4/src/dataloaders/audio.py index 035c8a9..5d60c9d 100644 --- a/s4/src/dataloaders/audio.py +++ b/s4/src/dataloaders/audio.py @@ -1090,9 +1090,9 @@ def init_defaults(self): def setup(self): - from s4_dx7.lib.data.audio_data_module import AudioDataModule + from s4_dx7.lightning.data.single_voice_to_voice import SingleVoice2VoiceDataModule - self.dataset = AudioDataModule( + self.dataset = SingleVoice2VoiceDataModule( bit_rate=self.bits, sr=self.sr, diff --git a/s4_dx7/lib/data/audio_data_module.py b/s4_dx7/lightning/data/multi_voice_to_voice.py similarity index 99% rename from s4_dx7/lib/data/audio_data_module.py rename to s4_dx7/lightning/data/multi_voice_to_voice.py index a5374a5..4d3182e 100644 --- a/s4_dx7/lib/data/audio_data_module.py +++ b/s4_dx7/lightning/data/multi_voice_to_voice.py @@ -12,7 +12,7 @@ def create_connection(): # duckdb.execute('install sqlite') return duckdb.connect(os.environ['AUDIO_DB']) -class AudioDataModule(): +class MultiVoice2VoiceDataModule(): def __init__(self, table='melodies', bit_rate=16, sr=44100, duration=2.5, limit=None): print(bit_rate, sr, duration) self.bit_rate = bit_rate diff --git a/s4_dx7/lightning/data/single_voice_to_voice.py b/s4_dx7/lightning/data/single_voice_to_voice.py new file mode 100644 index 0000000..f141189 --- /dev/null +++ b/s4_dx7/lightning/data/single_voice_to_voice.py @@ -0,0 +1,105 @@ +from functools import partial +import os +import duckdb +import pyarrow as pa +import ray +import numpy as np +import torch + +from s4_dx7.lib.render import render_batch + +def create_connection(): + # duckdb.execute('install sqlite') + return duckdb.connect(os.environ['AUDIO_DB']) + +class SingleVoice2VoiceDataModule(): + def __init__(self, table='melodies', bit_rate=16, sr=44100, duration=2.5, limit=None): + print(bit_rate, sr, duration) + self.bit_rate = bit_rate + self.duration = duration + self.sr = sr + self.table = table + self.limit = limit + self._tl = None + self._el = None + self._tel = None + def _dataset(self, dataset_type): + + limit_str = '' if self.limit is None else f'limit {self.limit}' + ds = ray.data.read_sql( + f'SELECT rowid FROM "{self.table}" order by rowid {limit_str}', + create_connection, + parallelism=1 + ) + train, test = ds.train_test_split(test_size=0.2) + test, validate = test.train_test_split(test_size=0.5) + f = partial(self.f, table=self.table, sr=self.sr, duration=self.duration, bit_rate=self.bit_rate) + pipeline = lambda dataset: dataset.repartition(200).map_batches(f, zero_copy_batch=True, batch_size=128) + if dataset_type=='train': + return pipeline(train) + elif dataset_type=='test': + return pipeline(test) + else: + return pipeline(validate) + + # self._dataset = dataset + @staticmethod + def f(batch, table, sr, duration, bit_rate): + + conn = create_connection() + batch_table = conn.execute(f''' + select notes from "{table}" + where rowid in (select rowid from batch) + ''').arrow() + + source_patch, target_patch = SingleVoice2VoiceDataModule.get_voices() + signals_source = render_batch(batch_table['notes'], sr, duration + 1/sr, voice=source_patch) + signals_target = render_batch(batch_table['notes'], sr, duration + 1/sr, voice=target_patch) + + delta_rate = 16-bit_rate + signals_target = SingleVoice2VoiceDataModule.clean_signal(signals_target, delta_rate, bit_rate) + signals_source = SingleVoice2VoiceDataModule.clean_signal(signals_source, delta_rate, bit_rate) + + return {"x": signals_source[:,:-1], "y": signals_target[:,:-1].unsqueeze(-1)} + + + @staticmethod + def clean_signal(signals, delta_rate, bit_rate): + + signals = torch.tensor(signals.to_pylist()) + bit_crushed = signals//(2**(delta_rate)) + bit_crushed = bit_crushed.clamp(0, 2**bit_rate-1) + return bit_crushed + + @staticmethod + def get_voices(): + target_patch, = create_connection().execute("select bytes from dx7_voices where path like '%SynprezFM_02%' and index=0").df().bytes + source_patch, = create_connection().execute("select bytes from dx7_voices where path like '%siine%' and index=0").df().bytes + return source_patch, target_patch + + def get_train_dataloader(self, batch_size): + if self._tl is None: + self._tl = self._dataset('train').iter_torch_batches( + batch_size=batch_size, + # prefetch_batches=2 + ) + return self._tl + + def get_eval_dataloader(self, batch_size): + + + if self._el is None: + self._el = self._dataset('validate').iter_torch_batches( + batch_size=batch_size, + # prefetch_batches=2 + ) + return self._el + + def get_test_dataloader(self, batch_size): + + if self._tel is None: + self._tel = self._dataset('test').iter_torch_batches( + batch_size=batch_size, + # prefetch_batches=2 + ) + return self._tel From 2d9095939f0e66929600bfdce821084a872925ce Mon Sep 17 00:00:00 2001 From: Nintorac Dev Date: Tue, 10 Sep 2024 15:32:58 +1000 Subject: [PATCH 05/11] [feat] added multivoice dataset in dbt --- s4_dx7_dbt/dbt_project.yml | 3 ++- .../models/dx7_patch/fsk_dx7_voices.sql | 2 ++ s4_dx7_dbt/models/melodies.sql | 4 ++- s4_dx7_dbt/models/multivoice_dataset.sql | 26 +++++++++++++++++++ s4_dx7_dbt/models/schema.yml | 5 ++-- 5 files changed, 35 insertions(+), 5 deletions(-) create mode 100644 s4_dx7_dbt/models/dx7_patch/fsk_dx7_voices.sql create mode 100644 s4_dx7_dbt/models/multivoice_dataset.sql diff --git a/s4_dx7_dbt/dbt_project.yml b/s4_dx7_dbt/dbt_project.yml index 3e45147..24f8262 100644 --- a/s4_dx7_dbt/dbt_project.yml +++ b/s4_dx7_dbt/dbt_project.yml @@ -39,8 +39,9 @@ models: melodies: # +materialized:`` incremental +database: audio - # TODO: find a nice way to send some things to different database. dx7_voices: +database: audio + multivoice_dataset: + +database: audio dx7_cartridges: +database: audio \ No newline at end of file diff --git a/s4_dx7_dbt/models/dx7_patch/fsk_dx7_voices.sql b/s4_dx7_dbt/models/dx7_patch/fsk_dx7_voices.sql new file mode 100644 index 0000000..43e28bd --- /dev/null +++ b/s4_dx7_dbt/models/dx7_patch/fsk_dx7_voices.sql @@ -0,0 +1,2 @@ +select voice_id, fsk_encode_syx(bytes) bytes +from {{ ref('distinct_dx7_voices') }} \ No newline at end of file diff --git a/s4_dx7_dbt/models/melodies.sql b/s4_dx7_dbt/models/melodies.sql index 24b9fb5..1c57c46 100644 --- a/s4_dx7_dbt/models/melodies.sql +++ b/s4_dx7_dbt/models/melodies.sql @@ -1,5 +1,7 @@ select - distinct to_json(list({ + distinct + phrase_id + , to_json(list({ 'start_time': e.start_time-floor(e.start_time/4)*4, 'note': e.note, 'duration': e.duration, diff --git a/s4_dx7_dbt/models/multivoice_dataset.sql b/s4_dx7_dbt/models/multivoice_dataset.sql new file mode 100644 index 0000000..44be410 --- /dev/null +++ b/s4_dx7_dbt/models/multivoice_dataset.sql @@ -0,0 +1,26 @@ +with dataset as ( + select + voice_id target_voice_id + , 'e7596651f46069a69e2773b7b004f9ae' source_voice_id + , phrase_id + , ( + select bytes + from {{ ref('distinct_dx7_voices') }} + -- sine patch + where voice_id='e7596651f46069a69e2773b7b004f9ae' + ) source_voice_bytes + , bytes target_voice_bytes + , notes midi_notes + , CASE + WHEN NTILE(10) OVER (ORDER BY voice_id) < 6 THEN 'train' + WHEN NTILE(10) OVER (ORDER BY voice_id) < 8 THEN 'test' + ELSE 'validate' + END AS data_split + FROM {{ ref('melodies') }} m + cross join {{ ref('distinct_dx7_voices') }} v +) +select + md5(target_voice_id || source_voice_id || phrase_id) dataset_item_id + , * +from dataset +order by dataset_item_id \ No newline at end of file diff --git a/s4_dx7_dbt/models/schema.yml b/s4_dx7_dbt/models/schema.yml index 3c3b2ab..ebe7c32 100644 --- a/s4_dx7_dbt/models/schema.yml +++ b/s4_dx7_dbt/models/schema.yml @@ -1,8 +1,7 @@ version: 2 models: -- name: render_midis +- name: multivoice_dataset config: materialized: table - partition_n: "{{ var('partition_n', 0) }}" - alias: render_midis_{{ var('partition_n', 0) }} \ No newline at end of file + \ No newline at end of file From d62992f6b9b42c8bb2aca64522321e0abc998f92 Mon Sep 17 00:00:00 2001 From: Nintorac Dev Date: Tue, 10 Sep 2024 15:34:23 +1000 Subject: [PATCH 06/11] [feat] added multivoice dataloader --- s4_dx7/lib/audio/__init__.py | 13 ++ s4_dx7/lib/audio/digital.py | 4 +- s4_dx7/lib/render.py | 47 ++++--- s4_dx7/lightning/__init__.py | 0 s4_dx7/lightning/data/__init__.py | 2 + s4_dx7/lightning/data/multi_voice_to_voice.py | 120 ++++++++++++------ s4_dx7/udf/__init__.py | 5 +- s4_dx7/udf/digital.py | 14 ++ s4_dx7/udf/render.py | 24 +++- s4_dx7/utils.py | 5 +- 10 files changed, 169 insertions(+), 65 deletions(-) create mode 100644 s4_dx7/lightning/__init__.py create mode 100644 s4_dx7/lightning/data/__init__.py create mode 100644 s4_dx7/udf/digital.py diff --git a/s4_dx7/lib/audio/__init__.py b/s4_dx7/lib/audio/__init__.py index e69de29..652d267 100644 --- a/s4_dx7/lib/audio/__init__.py +++ b/s4_dx7/lib/audio/__init__.py @@ -0,0 +1,13 @@ +import numpy +import torch + + +def float_to_pcm16(audio, bit_rate=16): + + ints = ((audio + 1) * (2**(bit_rate-1))).astype(numpy.int32) + + return ints + + +def pcm16_to_float(pcm16: torch.Tensor, bit_rate=16) -> torch.Tensor: + return (pcm16.float() / (2**(bit_rate-1)))-1 \ No newline at end of file diff --git a/s4_dx7/lib/audio/digital.py b/s4_dx7/lib/audio/digital.py index 9a76b19..b420b84 100644 --- a/s4_dx7/lib/audio/digital.py +++ b/s4_dx7/lib/audio/digital.py @@ -25,7 +25,7 @@ def fsk_encode_bytes_batch( # tones representing bits, dummy data (0,1) arrs = [] for item in encode_bytes: - bits = bitarray() + bits = bitarray(endian='big') bits.frombytes(item) bits_in_tones = [codebook[int(bit)] for bit in bits] arrs.append(torch.tensor(bits_in_tones)) @@ -43,7 +43,7 @@ def fsk_encode_syx( syx: List[bytearray], sampling_rate=44100, baud_rate=300 -): +) -> torch.Tensor: """ Encodes a syx message into an FSK signal adds some padding and a fade out to the end to avoid signal discontinuities diff --git a/s4_dx7/lib/render.py b/s4_dx7/lib/render.py index 6ab50dd..7c55ca9 100644 --- a/s4_dx7/lib/render.py +++ b/s4_dx7/lib/render.py @@ -1,3 +1,4 @@ +import numpy as np from functools import partial import io from itertools import chain @@ -8,8 +9,8 @@ from typing import List, Optional from mido import Message -import numpy from pedalboard import load_plugin +from s4_dx7.lib.audio import float_to_pcm16 from torchaudio.transforms import Resample from librosa import resample from tqdm import tqdm @@ -23,13 +24,6 @@ RENDER_SAMPLE_RATE = 44100 -def float_to_pcm16(audio, bit_rate=16): - - ints = ((audio + 1) * (2**(bit_rate-1))).astype(numpy.int32) - - return ints - - def to_midi(notes: List[dict], transpose:int=0, bpm:int=120)->List[Message]: # the note's are represented as start_time, duration. # But MIDI needs note_on, note_off tuples, @@ -42,7 +36,15 @@ def to_midi(notes: List[dict], transpose:int=0, bpm:int=120)->List[Message]: ) for note in notes])) # @ray.remote(num_cpus=1, memory=500 * 1024 * 1024) #5mb -def render_batch(notes: List[List[dict]], sr: int, duration: float, voice:Optional[bytes]=None, plugin: bytes=None, bpm: int=120)->pa.array: +def render_batch( + notes: List[List[dict]], + sr: int, + duration: float, + voice:Optional[bytes]=None, + plugin: bytes=None, + bpm: int=120, + pcm_encode: bool = True + )->pa.array: chunk = notes.to_pandas() # Load a VST3 or Audio Unit plugin from a known path on disk: if plugin: @@ -54,6 +56,7 @@ def render_batch(notes: List[List[dict]], sr: int, duration: float, voice:Option instrument = load_plugin(os.environ['DEFAULT_INSTRUMENT_PATH']) syx = [] if voice: + # TODO: is this neccesary? could use mido.Message.from_bytes? with NamedTemporaryFile('r+b') as f: f.write(voice) f.flush() @@ -71,28 +74,30 @@ def render_batch(notes: List[List[dict]], sr: int, duration: float, voice:Option to_midi_f = partial(to_midi, bpm=bpm) for notes in map(to_midi_f, chunk): - import time - from datetime import datetime - import logging - logger=logging.getLogger('lol') - logger.setLevel(logging.INFO) - st = time.time() - print(f'starting... {datetime.now()})') - + # raise ValueError(notes) + # synthesize then resample, the synthesis changes dramatically at different sample rates + # TODO: use this as another dimension of variance in a dataset x = instrument( notes, duration=duration, # seconds sample_rate=RENDER_SAMPLE_RATE, ) - print(time.time()-st) x = resample(x, orig_sr=RENDER_SAMPLE_RATE, target_sr=sr) - samples.append(float_to_pcm16(x.mean(0))) - # t.update(1) + # TODO: handle multi channel audio + render = x[0] + if pcm_encode: + samples.append(float_to_pcm16(render)) + continue + + samples.append(render) del instrument import gc gc.collect() # 1/0 - return pa.array(samples) + result = pa.array(samples) + # raise ValueError(result[0], samples[0].min(), samples[0].max()) + return result + diff --git a/s4_dx7/lightning/__init__.py b/s4_dx7/lightning/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/s4_dx7/lightning/data/__init__.py b/s4_dx7/lightning/data/__init__.py new file mode 100644 index 0000000..cad8532 --- /dev/null +++ b/s4_dx7/lightning/data/__init__.py @@ -0,0 +1,2 @@ +from .single_voice_to_voice import SingleVoice2VoiceDataModule +from .multi_voice_to_voice import MultiVoice2VoiceDataModule \ No newline at end of file diff --git a/s4_dx7/lightning/data/multi_voice_to_voice.py b/s4_dx7/lightning/data/multi_voice_to_voice.py index 4d3182e..ceaf860 100644 --- a/s4_dx7/lightning/data/multi_voice_to_voice.py +++ b/s4_dx7/lightning/data/multi_voice_to_voice.py @@ -1,3 +1,4 @@ +from enum import StrEnum from functools import partial import os import duckdb @@ -5,77 +6,118 @@ import ray import numpy as np import torch - +from torchaudio.functional import mu_law_encoding from s4_dx7.lib.render import render_batch +from s4_dx7.udf import Plugin +from s4_dx7.utils import get_duckdb_connection def create_connection(): # duckdb.execute('install sqlite') - return duckdb.connect(os.environ['AUDIO_DB']) + duck = duckdb.connect(os.environ['AUDIO_DB']) + Plugin.configure_connection(None, duck) + + return duck + +class DataSplit(StrEnum): + TRAIN = 'train' + TEST = 'test' + VALIDATE = 'validate' class MultiVoice2VoiceDataModule(): - def __init__(self, table='melodies', bit_rate=16, sr=44100, duration=2.5, limit=None): + def __init__(self, + table='multivoice_dataset', + bit_rate=16, + sr=44100, + duration=2.5, + limit=None, + patch_baud_rate=300 + + ): print(bit_rate, sr, duration) self.bit_rate = bit_rate self.duration = duration self.sr = sr self.table = table self.limit = limit + self.patch_baud_rate = patch_baud_rate self._tl = None self._el = None self._tel = None - def _dataset(self, dataset_type): + def _dataset(self, data_split): limit_str = '' if self.limit is None else f'limit {self.limit}' + data_split = DataSplit(data_split) ds = ray.data.read_sql( - f'SELECT rowid FROM "{self.table}" order by rowid {limit_str}', + f''' + SELECT rowid + FROM "{self.table}" + where data_split='{data_split.value}' + order by rowid {limit_str} + ''', create_connection, - parallelism=1 ) - train, test = ds.train_test_split(test_size=0.2) - test, validate = test.train_test_split(test_size=0.5) - f = partial(self.f, table=self.table, sr=self.sr, duration=self.duration, bit_rate=self.bit_rate) - pipeline = lambda dataset: dataset.repartition(200).map_batches(f, zero_copy_batch=True, batch_size=128) - if dataset_type=='train': - return pipeline(train) - elif dataset_type=='test': - return pipeline(test) - else: - return pipeline(validate) + if data_split == DataSplit.TRAIN: + ds = ds.random_shuffle() + + f = partial(self.f, + table=self.table, + sr=self.sr, + duration=self.duration, + bit_rate=self.bit_rate, + baud_rate=self.patch_baud_rate) + pipeline = ds.map_batches(f, zero_copy_batch=True, batch_size=5) + # raise ValueError(pipeline.stats()) + return pipeline # self._dataset = dataset @staticmethod - def f(batch, table, sr, duration, bit_rate): + def f(batch, table, sr, duration, bit_rate, baud_rate): conn = create_connection() batch_table = conn.execute(f''' - select notes from "{table}" - where rowid in (select rowid from batch) + with batch_data as ( + select * from "{table}" + where rowid in (select rowid from batch) + ) + select + source_voice_id + , target_voice_id + , phrase_id + , render_dx7( + midi_notes + , source_voice_bytes + , {sr} + , {duration} + ) source_voice , + render_dx7( + midi_notes + , target_voice_bytes + , {sr} + , {duration} + ) target_voice , + fsk_encode_syx(target_voice_bytes, {sr}, {baud_rate}) target_encoding + from batch_data ''').arrow() - source_patch, target_patch = AudioDataModule.get_voices() - signals_source = render_batch(batch_table['notes'], sr, duration + 1/sr, voice=source_patch) - signals_target = render_batch(batch_table['notes'], sr, duration + 1/sr, voice=target_patch) + target_encoding = torch.tensor(batch_table['target_encoding'].to_pylist()) + source_voice = torch.tensor(batch_table['source_voice'].to_pylist()) + target_voice = torch.tensor(batch_table['target_voice'].to_pylist()) - delta_rate = 16-bit_rate - signals_target = AudioDataModule.clean_signal(signals_target, delta_rate, bit_rate) - signals_source = AudioDataModule.clean_signal(signals_source, delta_rate, bit_rate) + signals_source = torch.cat([target_encoding, source_voice], dim=-1) + signals_target = torch.cat([torch.zeros_like(target_encoding), target_voice], dim=-1) - return {"x": signals_source[:,:-1], "y": signals_target[:,:-1].unsqueeze(-1)} + signals_target = mu_law_encoding(signals_target, bit_rate) + signals_source = mu_law_encoding(signals_source, bit_rate) + return { + "x": signals_source[:,:-1], + "y": signals_target[:,:-1].unsqueeze(-1), + "encoding": target_encoding, + # "source_voice_id": batch_table['source_voice_id'].to_pylist(), + # "target_voice_id": batch_table['target_voice_id'].to_pylist(), + # "phrase_id": batch_table['phrase_id'].to_pylist(), + } - @staticmethod - def clean_signal(signals, delta_rate, bit_rate): - - signals = torch.tensor(signals.to_pylist()) - bit_crushed = signals//(2**(delta_rate)) - bit_crushed = bit_crushed.clamp(0, 2**bit_rate-1) - return bit_crushed - - @staticmethod - def get_voices(): - target_patch, = create_connection().execute("select bytes from dx7_voices where path like '%SynprezFM_02%' and index=0").df().bytes - source_patch, = create_connection().execute("select bytes from dx7_voices where path like '%siine%' and index=0").df().bytes - return source_patch, target_patch def get_train_dataloader(self, batch_size): if self._tl is None: diff --git a/s4_dx7/udf/__init__.py b/s4_dx7/udf/__init__.py index b49fb7b..5fd9238 100644 --- a/s4_dx7/udf/__init__.py +++ b/s4_dx7/udf/__init__.py @@ -1,13 +1,16 @@ import asyncio from duckdb import DuckDBPyConnection from dbt.adapters.duckdb.plugins import BasePlugin +from s4_dx7.udf.digital import fsk_encode_syx_udf from s4_dx7.udf.dx7 import clean_voice_udf, extract_voices_udf -from s4_dx7.udf.render import render_midi_udf +from s4_dx7.udf.render import render_dx7_udf, render_midi_udf class Plugin(BasePlugin): def configure_connection(self, conn: DuckDBPyConnection): conn.create_function("render_midi", render_midi_udf, type="arrow") conn.create_function("extract_voices", extract_voices_udf, type="arrow") conn.create_function("clean_voice", clean_voice_udf, type="arrow") + conn.create_function("fsk_encode_syx", fsk_encode_syx_udf, type="arrow") + conn.create_function("render_dx7", render_dx7_udf, type="arrow") \ No newline at end of file diff --git a/s4_dx7/udf/digital.py b/s4_dx7/udf/digital.py new file mode 100644 index 0000000..eb216e3 --- /dev/null +++ b/s4_dx7/udf/digital.py @@ -0,0 +1,14 @@ +from typing import List +import duckdb +from duckdb.typing import BLOB, DuckDBPyType, BIGINT +from s4_dx7.lib.audio.digital import fsk_encode_syx +from s4_dx7.lib.dx7 import clean_voice +from s4_dx7.lib.dx7 import extract_voices + +AUDIO_TYPE = DuckDBPyType(list[float]) +def fsk_encode_syx_udf(bytes_to_encode: BLOB, sampling_rate: BIGINT, baud_rate: BIGINT)->AUDIO_TYPE: + if not len(set(sampling_rate))==len(set(baud_rate))==1: + raise ValueError('cant process different sample/baud rates in a single batch') + result = fsk_encode_syx(bytes_to_encode.to_pandas(), sampling_rate[0].as_py(), baud_rate[0].as_py()) + result = list(result.numpy()) + return result diff --git a/s4_dx7/udf/render.py b/s4_dx7/udf/render.py index c05cd1c..4582549 100644 --- a/s4_dx7/udf/render.py +++ b/s4_dx7/udf/render.py @@ -3,6 +3,7 @@ from tempfile import NamedTemporaryFile from typing import List +import pyarrow from mido import Message from pedalboard import load_plugin from tqdm import tqdm @@ -10,9 +11,10 @@ import ray import pyarrow as pa import duckdb -from duckdb.typing import VARCHAR, DuckDBPyType, BLOB +from duckdb.typing import VARCHAR, DuckDBPyType, BLOB, INTEGER, FLOAT from s4_dx7.lib.render import render_batch +from s4_dx7.udf.digital import AUDIO_TYPE # AUDIO = DuckDBPyType(List[str]) def render_midi_udf(batch: VARCHAR)->BLOB: @@ -27,3 +29,23 @@ def render_midi_udf(batch: VARCHAR)->BLOB: chunks.append(chunk) return pa.concat_arrays(ray.get(chunks)) + +def render_dx7_udf( + notes: VARCHAR, + voice: BLOB, + sr: INTEGER, + duration: FLOAT + )->AUDIO_TYPE: + """ + probably not super performant at scale but good enough for training + """ + promises = [] + f = ray.remote(num_cpus=1, memory=500 * 1024 * 1024)(render_batch) + a = lambda x: pyarrow.array([x]) + for n, v, s, d in zip(notes, voice, sr, duration): + promise = f.remote(a(n), s.as_py(), d.as_py(), v.as_py(), pcm_encode=False) + promises.append(promise) + + # return pa.array(ray.get(promises)) + results = ray.get(promises) + return pa.concat_arrays(results) diff --git a/s4_dx7/utils.py b/s4_dx7/utils.py index 45b65b1..b262e1d 100644 --- a/s4_dx7/utils.py +++ b/s4_dx7/utils.py @@ -1,8 +1,10 @@ +from functools import lru_cache +import os import duckdb from s4_dx7.udf import Plugin - +# @lru_cache def get_duckdb_connection(path=':memory:'): """ inits the duckdb connection according to the dbt setup @@ -12,4 +14,5 @@ def get_duckdb_connection(path=':memory:'): """ conn = duckdb.connect(path) Plugin.configure_connection(None, conn) + conn.execute(f"attach '{os.environ['AUDIO_DB']}' as audio") return conn \ No newline at end of file From 6eb20711ef40f77bf73e1368cfb411196cb6540a Mon Sep 17 00:00:00 2001 From: Nintorac Dev Date: Tue, 10 Sep 2024 15:35:33 +1000 Subject: [PATCH 07/11] [feat] added multivoice analysis notebooks --- notebooks/dataloaders/multi_voice2voice.py | 96 ++++++++++++++++++++++ notebooks/fsk_patch_visualisations.py | 45 ++++++---- 2 files changed, 126 insertions(+), 15 deletions(-) create mode 100644 notebooks/dataloaders/multi_voice2voice.py diff --git a/notebooks/dataloaders/multi_voice2voice.py b/notebooks/dataloaders/multi_voice2voice.py new file mode 100644 index 0000000..b00b560 --- /dev/null +++ b/notebooks/dataloaders/multi_voice2voice.py @@ -0,0 +1,96 @@ +#%% [markdown] +# This page is a render of the notebook found at `notebooks/dataloaders/multi_voice2voice.py` in the [source repo](https://github.com/Nintorac/s4_dx7) + +# > [!warning] +# > **The audio can be quite loud**: Turn down your volume + +# It shows several random examples of data used in the training of [[s4-dx7-vc-fir-03]] +# %% +import torch +import librosa +import librosa.display +import matplotlib.pyplot as plt +from IPython.display import Audio, display +import numpy as np +import pandas as pd +import scipy.signal as s +from s4_dx7.lightning.data import MultiVoice2VoiceDataModule +from s4_dx7.lib.visualistaion.audio import waveform_segment_figure +from torchaudio.functional import mu_law_decoding +from s4_dx7.notebook.mel_spec_audio import plot_melspectrogram_and_play_button +## TODO figure out how to remove error ouput from rendered document +#%% +#%% +# Define a helper function to plot the mel spectrogram and create the play button +bit_rate=8 +sr=40000 +baud=8000 +sample_size=15 + +dt = int(1/baud*sr) # samples per bit +encoding_duration = (dt*8*155)/sr +encoding_samples = dt*8*155 + +data_module = MultiVoice2VoiceDataModule(bit_rate=bit_rate, limit=sample_size, sr=sr, patch_baud_rate=baud) +# Iterate over each batch (assuming a single batch here for simplicity) +for batch in data_module.get_train_dataloader(sample_size): + batch_size = batch['x'].shape[0] + + for i in range(5): + # Select the i-th sample from both 'x' and 'y' signals in the batch + x_signal = batch['x'][i] + y_signal = batch['y'][i].squeeze(-1) + # raise ValueError(y_signal.min(), y_signal.max()) + y_signal = mu_law_decoding(y_signal, bit_rate) + x_signal = mu_law_decoding(x_signal, bit_rate) + + # print(f"source voice - {batch['source_voice_id'][i]}") + # print(f"target voice - {batch['target_voice_id'][i]}") + # print(f"phrase - {batch['phrase_id'][i]}") + # Plot and display encoding + print(f"Playing and displaying encoding") + plot_melspectrogram_and_play_button(batch['encoding'][i], sr) + + # Plot and display x + print(f"Playing and displaying x[{i}]") + plot_melspectrogram_and_play_button(x_signal, sr) + + # plot and display y + print(f"playing and displaying y[{i}]") + plot_melspectrogram_and_play_button(y_signal, sr) + + wave_fig = waveform_segment_figure( + x_signal, + sr, + ( + (0, (dt*16*8)/sr), # first 16 bytes of encoding + (encoding_duration, encoding_duration+1), # 1s of audio + (encoding_duration+1, encoding_duration+1.1), # 0.1s of audio + (encoding_duration+1, encoding_duration+1.01), # 0.01s of audio + ), + title="source waveforms - first 16 bytes of encoding, 1s of audio, 0.1s of audio, 0.01s of audio" + ) + display(wave_fig) + plt.close("all") # supress inlined plots https://stackoverflow.com/questions/49545003/how-to-suppress-matplotlib-inline-for-a-single-cell-in-jupyter-notebooks-lab + wave_fig = waveform_segment_figure( + y_signal, + sr, + ( + (encoding_duration, encoding_duration+1), # first second of audio source + (encoding_duration+1, encoding_duration+1.1), # 0.1s of audio + (encoding_duration+1, encoding_duration+1.01), # 0.01s of audio + ), + title="target waveforms - 1s of audio, 0.1s of audio, 0.01s of audio" + ) + display(wave_fig) + plt.close("all") # supress inlined plots https://stackoverflow.com/questions/49545003/how-to-suppress-matplotlib-inline-for-a-single-cell-in-jupyter-notebooks-lab + + t = np.arange(0, x_signal.shape[0] / sr, 1.0 / sr) + f, t_s, Zxx = s.stft(x_signal, nfft=128, fs=sr, nperseg=4, noverlap=3, padded=True) + + plt.pcolormesh(t_s[:encoding_samples//8], f, np.abs(Zxx[...,:encoding_samples//8])) + plt.title(f"{encoding_samples//8} (~19 bytes) samples of the patch encoding as a spectrogram") + plt.show() + + plt.close("all") # supress inlined plots https://stackoverflow.com/questions/49545003/how-to-suppress-matplotlib-inline-for-a-single-cell-in-jupyter-notebooks-lab + break diff --git a/notebooks/fsk_patch_visualisations.py b/notebooks/fsk_patch_visualisations.py index 801f045..f29e345 100644 --- a/notebooks/fsk_patch_visualisations.py +++ b/notebooks/fsk_patch_visualisations.py @@ -1,4 +1,7 @@ #%% +from itertools import chain + +from bitarray import bitarray from s4_dx7.lib.audio.digital import fsk_encode_bytes_batch import matplotlib.pyplot as plt import numpy as np @@ -10,8 +13,8 @@ duck = get_duckdb_connection('data/dev.duckdb') #%% signal = fsk_encode_bytes_batch([b'lB', b'ab'], sampling_rate=8000) -sampling_rate = 16000 -baud_rate = sampling_rate//8 +sampling_rate = 40000 +baud_rate = 8000 t = np.arange(0, signal.shape[1] / sampling_rate, 1.0 / sampling_rate) plt.plot(t, signal[0].numpy()+1) plt.plot(t, signal[1].numpy()-1) @@ -24,22 +27,34 @@ signal = fsk_encode_syx(voices.bytes, sampling_rate=sampling_rate, baud_rate=baud_rate) print(signal.shape) #%% -truncate = 200 -skip = 500 +dt = int(1/baud_rate*sampling_rate) # samples per bit +truncate = dt * 32 +skip = dt * 64 +#%% +bits = bitarray(endian='big') +bits.frombytes(voices.bytes[0]) +bytes_as_bits = chain.from_iterable(map(int, list(format(bin(25), '010')[2:])) for byte in voices.bytes[0]) +bits_arr = np.array(list(bits)).repeat(dt) +# bits_arr = np.array(list(bytes_as_bits)).repeat(dt) +bits_arr_w = np.stack([bits_arr]* 500) +plt.imshow(bits_arr_w[skip:skip+truncate]) +# %% + +fig, (signal_ax, st_ax, spec_ax) = plt.subplots((3), sharex=True) + t = np.arange(0, signal.shape[1] / sampling_rate, 1.0 / sampling_rate)[skip:skip+truncate] -dt = 1/baud_rate*sampling_rate # your desired spacing x_locations = np.arange(skip, skip+truncate, dt)/sampling_rate for x in x_locations: - plt.axvline(x=x, color='k', linestyle='--') + signal_ax.axvline(x=x, color='k', linestyle='--') + st_ax.axvline(x=x, color='k', linestyle='--') + spec_ax.axvline(x=x, color='k', linestyle='--') +st_ax.plot(t, signal[0, skip:skip+truncate].numpy()) +# st_ax.plot(t, signal[1, skip:skip+truncate].numpy()-1.5) +signal_ax.plot(t, bits_arr[skip:skip+truncate]-0.5) -plt.plot(t, signal[0, skip:skip+truncate].numpy()+1) -plt.plot(t, signal[1, skip:skip+truncate].numpy()-1) -#%% - -for x in x_locations: - plt.axvline(x=x, color='k', linestyle='--') -f, t, Zxx = s.stft(signal[0, skip:skip+truncate], nfft=128, fs=sampling_rate, nperseg=dt//2, noverlap=dt//4, padded=True) -plt.pcolormesh(t+skip/sampling_rate, f, np.abs(Zxx)) +f, t_s, Zxx = s.stft(signal[0], nfft=128, fs=sampling_rate, nperseg=4, noverlap=3, padded=True) +valid_ts = (t_s=t.min()) +spec_ax.pcolormesh(t_s[valid_ts], f, np.abs(Zxx[...,valid_ts])) +# spec_ax.pcolormesh(t_s, f, np.abs(Zxx)) # %% - From cdb2e9385ad3f62c7e87c68932896991276b6172 Mon Sep 17 00:00:00 2001 From: Nintorac Dev Date: Tue, 10 Sep 2024 15:36:47 +1000 Subject: [PATCH 08/11] [chore] update deps to allow for notebook rendering --- .gitignore | 4 +- Makefile | 3 + poetry.lock | 1255 +++++++++++++++++++++++++++++------------------- pyproject.toml | 10 +- 4 files changed, 772 insertions(+), 500 deletions(-) diff --git a/.gitignore b/.gitignore index 092bb71..52f600f 100644 --- a/.gitignore +++ b/.gitignore @@ -33,4 +33,6 @@ carts/ experiments/ -.obsidian/ \ No newline at end of file +.obsidian/ + +*.ipynb \ No newline at end of file diff --git a/Makefile b/Makefile index 00327e3..450a787 100644 --- a/Makefile +++ b/Makefile @@ -6,3 +6,6 @@ install_data_deps: poetry install --only vis --only data +render_multivoice_notebook: + jupytext --to ipynb notebooks/dataloaders/multi_voice2voice.py + jupyter nbconvert --to markdown --execute notebooks/dataloaders/multi_voice2voice.ipynb --no-input --output-dir='./docs/experiment_logs/dataset_visualisations' diff --git a/poetry.lock b/poetry.lock index 6bc3f95..574ae7f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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= "^2.1.2" +torchaudio = "^2.1.2" +torchvision = "^0.16.2" +torchtext = "^0.16.2" pyarrow = "^14.0.2" pandas = "^2.1.4" hydra-core = "^1.3.2" @@ -32,11 +36,7 @@ numpy = "<2" python-rtmidi = "^1.5.8" [tool.poetry.group.training.dependencies] -torchaudio = "^2.1.2" -torchvision = "^0.16.2" -torchtext = "^0.16.2" triton = ">=2.1.0" -torch = "^2.1.2" datasets = "^2.16.1" pytorch-lightning = "2.0.4" transformers = "^4.36.2" @@ -52,6 +52,8 @@ plotly = "^5.18.0" nbformat = "^5.9.2" ipykernel = "^6.28.0" pyvis = "^0.3.2" +jupytext = "^1.16.4" +nbconvert = "^7.16.4" [tool.poetry.group.debug.dependencies] From 634aa8de8846a284b8c61e92bf1ad90095065c2d Mon Sep 17 00:00:00 2001 From: Nintorac Dev Date: Tue, 10 Sep 2024 15:38:36 +1000 Subject: [PATCH 09/11] [docs] added inital s4-dx7-vc-fir-03 documentation --- .../multi_voice2voice.md | 412 ++++++++++++++++++ .../multi_voice2voice_3_12.png | 3 + .../multi_voice2voice_3_15.png | 3 + .../multi_voice2voice_3_18.png | 3 + .../multi_voice2voice_3_20.png | 3 + .../multi_voice2voice_3_21.png | 3 + .../multi_voice2voice_3_22.png | 3 + .../multi_voice2voice_3_24.png | 3 + .../multi_voice2voice_3_27.png | 3 + .../multi_voice2voice_3_30.png | 3 + .../multi_voice2voice_3_32.png | 3 + .../multi_voice2voice_3_33.png | 3 + .../multi_voice2voice_3_34.png | 3 + .../multi_voice2voice_3_36.png | 3 + .../multi_voice2voice_3_39.png | 3 + .../multi_voice2voice_3_42.png | 3 + .../multi_voice2voice_3_44.png | 3 + .../multi_voice2voice_3_45.png | 3 + .../multi_voice2voice_3_46.png | 3 + .../multi_voice2voice_3_48.png | 3 + .../multi_voice2voice_3_51.png | 3 + .../multi_voice2voice_3_54.png | 3 + .../multi_voice2voice_3_56.png | 3 + .../multi_voice2voice_3_57.png | 3 + .../multi_voice2voice_3_58.png | 3 + .../multi_voice2voice_3_60.png | 3 + .../multi_voice2voice_3_63.png | 3 + .../multi_voice2voice_3_66.png | 3 + .../multi_voice2voice_3_68.png | 3 + .../multi_voice2voice_3_69.png | 3 + .../multi_voice2voice_3_70.png | 3 + docs/experiment_logs/s4-dx7-vc-fir-03.md | 104 +++++ 32 files changed, 606 insertions(+) create mode 100644 docs/experiment_logs/dataset_visualisations/multi_voice2voice.md create mode 100644 docs/experiment_logs/dataset_visualisations/multi_voice2voice_files/multi_voice2voice_3_12.png create mode 100644 docs/experiment_logs/dataset_visualisations/multi_voice2voice_files/multi_voice2voice_3_15.png create mode 100644 docs/experiment_logs/dataset_visualisations/multi_voice2voice_files/multi_voice2voice_3_18.png create mode 100644 docs/experiment_logs/dataset_visualisations/multi_voice2voice_files/multi_voice2voice_3_20.png create mode 100644 docs/experiment_logs/dataset_visualisations/multi_voice2voice_files/multi_voice2voice_3_21.png create mode 100644 docs/experiment_logs/dataset_visualisations/multi_voice2voice_files/multi_voice2voice_3_22.png create mode 100644 docs/experiment_logs/dataset_visualisations/multi_voice2voice_files/multi_voice2voice_3_24.png create mode 100644 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docs/experiment_logs/dataset_visualisations/multi_voice2voice_files/multi_voice2voice_3_58.png create mode 100644 docs/experiment_logs/dataset_visualisations/multi_voice2voice_files/multi_voice2voice_3_60.png create mode 100644 docs/experiment_logs/dataset_visualisations/multi_voice2voice_files/multi_voice2voice_3_63.png create mode 100644 docs/experiment_logs/dataset_visualisations/multi_voice2voice_files/multi_voice2voice_3_66.png create mode 100644 docs/experiment_logs/dataset_visualisations/multi_voice2voice_files/multi_voice2voice_3_68.png create mode 100644 docs/experiment_logs/dataset_visualisations/multi_voice2voice_files/multi_voice2voice_3_69.png create mode 100644 docs/experiment_logs/dataset_visualisations/multi_voice2voice_files/multi_voice2voice_3_70.png create mode 100644 docs/experiment_logs/s4-dx7-vc-fir-03.md diff --git a/docs/experiment_logs/dataset_visualisations/multi_voice2voice.md b/docs/experiment_logs/dataset_visualisations/multi_voice2voice.md new file mode 100644 index 0000000..bf1b970 --- /dev/null +++ b/docs/experiment_logs/dataset_visualisations/multi_voice2voice.md @@ -0,0 +1,412 @@ +This page is a render of the notebook found at `notebooks/dataloaders/multi_voice2voice.py` in the [source repo](https://github.com/Nintorac/s4_dx7) + +> [!warning] +> **The audio can be quite loud**: Turn down your volume + +It shows several random examples of data used in the training of [[s4-dx7-vc-fir-03]] + + /opt/conda/envs/py3115/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html + from .autonotebook import tqdm as notebook_tqdm + 2024-09-10 05:28:22,583 INFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output. + + + /opt/conda/envs/py3115/lib/python3.11/site-packages/pydub/utils.py:170: RuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work + warn("Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work", RuntimeWarning) + + + 2024-09-10 05:28:24,023 INFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output. + + + 8 40000 2.5 + + + 2024-09-10 05:28:25,255 WARNING services.py:2017 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 67108864 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=9.46gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM. + + + 2024-09-10 05:28:26,338 INFO worker.py:1774 -- Started a local Ray instance. View the dashboard at http://127.0.0.1:8265  + + + (_get_datasource_or_legacy_reader pid=217729) /opt/conda/envs/py3115/lib/python3.11/site-packages/pydub/utils.py:170: RuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work + (_get_datasource_or_legacy_reader pid=217729) warn("Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work", RuntimeWarning) + + + 2024-09-10 05:28:31,755 INFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output. + + + 2024-09-10 05:28:33,150 INFO streaming_executor.py:108 -- Starting execution of Dataset. Full logs are in /tmp/ray/session_2024-09-10_05-28-24_036670_217285/logs/ray-data + + + 2024-09-10 05:28:33,151 INFO streaming_executor.py:109 -- Execution plan of Dataset: InputDataBuffer[Input] -> TaskPoolMapOperator[ReadSQL] -> AllToAllOperator[RandomShuffle] -> TaskPoolMapOperator[MapBatches(partial)] + + + (MapBatches(partial) pid=217721) /opt/conda/envs/py3115/lib/python3.11/site-packages/pydub/utils.py:170: RuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work + (MapBatches(partial) pid=217721) warn("Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work", RuntimeWarning) + + + (MapBatches(partial) pid=217722) /opt/conda/envs/py3115/lib/python3.11/site-packages/pydub/utils.py:170: RuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work + (MapBatches(partial) pid=217722) warn("Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work", RuntimeWarning) + + + (reduce pid=217719) /opt/conda/envs/py3115/lib/python3.11/site-packages/pydub/utils.py:170: RuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work [repeated 3x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.) + (reduce pid=217719) warn("Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work", RuntimeWarning) [repeated 3x across cluster] + + + Playing and displaying encoding + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_12.png) + + + + + + + + + + Playing and displaying x[0] + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_15.png) + + + + + + + + + + playing and displaying y[0] + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_18.png) + + + + + + + + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_20.png) + + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_21.png) + + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_22.png) + + + + Playing and displaying encoding + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_24.png) + + + + + + + + + + Playing and displaying x[1] + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_27.png) + + + + + + + + + + playing and displaying y[1] + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_30.png) + + + + + + + + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_32.png) + + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_33.png) + + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_34.png) + + + + Playing and displaying encoding + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_36.png) + + + + + + + + + + Playing and displaying x[2] + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_39.png) + + + + + + + + + + playing and displaying y[2] + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_42.png) + + + + + + + + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_44.png) + + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_45.png) + + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_46.png) + + + + Playing and displaying encoding + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_48.png) + + + + + + + + + + Playing and displaying x[3] + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_51.png) + + + + + + + + + + playing and displaying y[3] + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_54.png) + + + + + + + + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_56.png) + + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_57.png) + + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_58.png) + + + + Playing and displaying encoding + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_60.png) + + + + + + + + + + Playing and displaying x[4] + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_63.png) + + + + + + + + + + playing and displaying y[4] + + + + +![png](multi_voice2voice_files/multi_voice2voice_3_66.png) + + + + + + + + + + + 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b/docs/experiment_logs/dataset_visualisations/multi_voice2voice_files/multi_voice2voice_3_70.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:81409f394f1b3d7b8de3ba933ad1bf354712adac111e04355e3f1c3938c973f1 +size 38099 diff --git a/docs/experiment_logs/s4-dx7-vc-fir-03.md b/docs/experiment_logs/s4-dx7-vc-fir-03.md new file mode 100644 index 0000000..4fc500f --- /dev/null +++ b/docs/experiment_logs/s4-dx7-vc-fir-03.md @@ -0,0 +1,104 @@ + + +# Goals + +- recover performance gap with s4 + - mamba2 hp tuning + - understand mamba2 init +- dataset improvements + - multivoice + - audio patch encoding (to use as conditioning) + + +## Details + +The bugfixes were simple, I think going forward I should lean more heavily on the torch audio library,since the last time I looked at it it has progressed far, partiuclarly the mu-law encoding looks much better than my bitcrushing. + +The Mamba2 integration was bit more tricky, there are some tricky dependencies between causalconv1d and mamba which took a couple reinstalls to work through and still not really sure how I actually fixed the problem, will have to figure it out again next time i delete my lambda instance. + +The only other gotcha is to make sure that (d_head \times expansion) / n_heads is a multiple of 8, otherwise you will get various issues + +these were the issues I visited +- [mamba2 training speed is very very very slow · Issue #389 · state-spaces/mamba](https://github.com/state-spaces/mamba/issues/389) +- [On the small model, the actual GPU memory usage of Mamba2 is much higher than that of Mamba1. · Issue #439 · state-spaces/mamba](https://github.com/state-spaces/mamba/issues/439) +- [How to load mamba1's weight to mamba2 ? · Issue #453 · state-spaces/mamba](https://github.com/state-spaces/mamba/issues/453) +- [How to use Mamba2？ · Issue #475 · state-spaces/mamba](https://github.com/state-spaces/mamba/issues/475) [Slow Mamba 2 training speeds with higher d_state values · Issue #479 · state-spaces/mamba](https://github.com/state-spaces/mamba/issues/479) +- [why GPU memory cost of Mamba2 Block > full self-attention block? And how to reduce this memory cost when training? · Issue #495 · state-spaces/mamba](https://github.com/state-spaces/mamba/issues/495) + - [CUDA error when using Mamba2 with long context · Issue #503 · state-spaces/mamba](https://github.com/state-spaces/mamba/issues/503) +- [Replace mamba1 with mamba2 and training becomes very slow! · Issue #510 · state-spaces/mamba](https://github.com/state-spaces/mamba/issues/510) +- [Loss NaN in Mamba2 · Issue #352 · state-spaces/mamba](https://github.com/state-spaces/mamba/issues/352) +- [训练时loss.backward出现问题了 · Issue #373 · state-spaces/mamba](https://github.com/state-spaces/mamba/issues/373) +- [The simple test of the model works fine, but there is an "Aborted (core dumped)" issue during training. · Issue #375 · state-spaces/mamba](https://github.com/state-spaces/mamba/issues/375) +- [causal-conv1d installation error · Issue #55 · state-spaces/mamba](https://github.com/state-spaces/mamba/issues/55) +- [Import Error · Issue #169 · state-spaces/mamba](https://github.com/state-spaces/mamba/issues/169#issue-2122414656) +- [Mamba-2 Error: `'NoneType' object has no attribute 'causal_conv1d_fwd'` · Issue #467 · state-spaces/mamba](https://github.com/state-spaces/mamba/issues/467) +- [mamba_ssm Install Failure · Issue #481 · state-spaces/mamba](https://github.com/state-spaces/mamba/issues/481) + +## Results + +The bugfixes and Mamba2 integration were implemented and a model was trained using the same settings as [[s4-dx7-vc-fir-01]] but using the Mamba2 layer rather than S4 + +Results from this run are `exalted-flower-180`, the model from [[s4-dx7-vc-fir-01]] is `snowy-sponge-110` + +### loss +The trainer loss looks like this (note `snowy-sponge` is truncated here and actual run went for many more steps). It is starting much higher than the S4 model but seems to be descending at a faster rate. + +the higher starting loss could have many reasons +- more difficult problem without the signal truncation bug +- bad initialisation + - S4 goes into extreme care to init the weights just right, not sure if the `mamba-ssm` module I'm using is handling this or if its necessary for the transformer style ssm +- bad hyper parameters +- broken assumptions + - in the theory it states that mamba works better for discrete domains and s4 better for continuous domains + - imo opinion unlikely to be the reason +![[train_loss.png]] + + +### step time +The per step time has increased about 0.1s between the S4 and the Mamba2, I know Mamba2 is supposed to be faster than Mamba but I also think it makes sense that S4 is faster +![[step_time.png]] + +### validation results + +The numbers here look about right. Worse than the S4 with a worse loss. + +it is interesting that the S4 is much smoother in the validation results, to me this makes me further suspicious that my current initialisation is not correct. +![[validation_plots.png]] + +### inferences + +#### source + + + +![[s4-dx7-vc-fir-02-source-signal.png]] +![[s4-dx7-vc-fir-02-source-mel.png]] + + + +#### target + + + +![[s4-dx7-vc-fir-02-target-signal.png]] +![[s4-dx7-vc-fir-02-target-mel.png]] + + + +#### generated + + + +![[s4-dx7-vc-fir-02-generated-signal.png]] +![[s4-dx7-vc-fir-02-generated-mel.png]] + + +# ideas + +- encode patch bytes into audio stream and prepend to the synthesized source audio + - opens possibility for multivoice + - simple and does not require architectural changes to deal with the condtioning + - follows effective patterns from LLMs around in context learning + - likely not possible with S4 due to time independent computing..mamba though + - can use FM synth as the configuration signal heh +- Train a Mamba1 variant as well From ea095e748642c7d83b344d8a5325255efeb93d0b Mon Sep 17 00:00:00 2001 From: Nintorac Dev Date: Tue, 10 Sep 2024 13:55:41 +0000 Subject: [PATCH 10/11] wip --- notebooks/dataloaders/multi_voice2voice.py | 12 +-- .../audio/sashimi-dx7-vc-fir-03.yaml | 86 +++++++++++++++++++ .../experiment/audio/sashimi-dx7-vc-fir.yaml | 8 +- s4/src/dataloaders/audio.py | 18 +--- s4_dx7/lib/layers/mamba2.py | 8 +- s4_dx7/lightning/data/multi_voice_to_voice.py | 17 +++- 6 files changed, 114 insertions(+), 35 deletions(-) create mode 100644 s4/configs/experiment/audio/sashimi-dx7-vc-fir-03.yaml diff --git a/notebooks/dataloaders/multi_voice2voice.py b/notebooks/dataloaders/multi_voice2voice.py index b00b560..43707f9 100644 --- a/notebooks/dataloaders/multi_voice2voice.py +++ b/notebooks/dataloaders/multi_voice2voice.py @@ -1,9 +1,9 @@ #%% [markdown] # This page is a render of the notebook found at `notebooks/dataloaders/multi_voice2voice.py` in the [source repo](https://github.com/Nintorac/s4_dx7) - +# # > [!warning] # > **The audio can be quite loud**: Turn down your volume - +# # It shows several random examples of data used in the training of [[s4-dx7-vc-fir-03]] # %% import torch @@ -38,8 +38,8 @@ for i in range(5): # Select the i-th sample from both 'x' and 'y' signals in the batch - x_signal = batch['x'][i] - y_signal = batch['y'][i].squeeze(-1) + x_signal = batch['x'][i].cpu() + y_signal = batch['y'][i].squeeze(-1).cpu() # raise ValueError(y_signal.min(), y_signal.max()) y_signal = mu_law_decoding(y_signal, bit_rate) x_signal = mu_law_decoding(x_signal, bit_rate) @@ -49,7 +49,7 @@ # print(f"phrase - {batch['phrase_id'][i]}") # Plot and display encoding print(f"Playing and displaying encoding") - plot_melspectrogram_and_play_button(batch['encoding'][i], sr) + plot_melspectrogram_and_play_button(batch['encoding'][i].cpu(), sr) # Plot and display x print(f"Playing and displaying x[{i}]") @@ -94,3 +94,5 @@ plt.close("all") # supress inlined plots https://stackoverflow.com/questions/49545003/how-to-suppress-matplotlib-inline-for-a-single-cell-in-jupyter-notebooks-lab break + +# %% diff --git a/s4/configs/experiment/audio/sashimi-dx7-vc-fir-03.yaml b/s4/configs/experiment/audio/sashimi-dx7-vc-fir-03.yaml new file mode 100644 index 0000000..583d889 --- /dev/null +++ b/s4/configs/experiment/audio/sashimi-dx7-vc-fir-03.yaml @@ -0,0 +1,86 @@ +# @package _global_ +defaults: + - /trainer: default + - /loader: default + - /dataset: dx7 + - /task: multiclass_classification + - /optimizer: adamw + - /scheduler: plateau + - /model: sashimi + - override /model/layer: mamba2 +model: + n_layers: 8 + dropout: 0.0 + d_model: 128 + layer: + # nheads = (self.expand * self.d_model) // self.headdim + # = 128*2//(16) + # = 16 + # note nheads must divide 8 + # and nheads//8 more than 1 (i think there is a squeeze bug somewhere) + d_state: 128 + d_conv: 4 + conv_init: null + expand: 2 + headdim: 16 + d_ssm: null # If not None, we only apply SSM on this many dimensions, the rest uses gated MLP + ngroups: 1 + A_init_range: + - 1. + - 1.1 + D_has_hdim: false + rmsnorm: true + norm_before_gate: false + dt_min: 0.001 + dt_max: 0.1 + dt_init_floor: 1e-4 + dt_limit: + - 0.0 + - .inf + bias: false + conv_bias: true + # Fused kernel and sharding options + # chunk_size: 256 + # use_mem_eff_path: True + # process_group: None + # sequence_parallel: True + # device: None + # dtype: None + +train: + monitor: val/loss + mode: min + +task: + metrics: + - bpb + - accuracy + - accuracy@3 + - accuracy@5 + - accuracy@10 + +encoder: embedding + +decoder: + _name_: sequence + mode: last + +loader: + batch_size: 2 + +trainer: + max_epochs: 1000 + limit_train_batches: 1000 + limit_val_batches: 100 + accumulate_grad_batches: 2 + +dataset: + sr: 40000 + duration: 2.164 # TODO: hack, duration control should account for encoding + data_module: + _target_: s4_dx7.lightning.data.multi_voice_to_voice.MultiVoice2VoiceDataModule + sr: 40000 + duration: 2 + bit_rate: 10 + patch_baud_rate: 8000 + limit: 1000 diff --git a/s4/configs/experiment/audio/sashimi-dx7-vc-fir.yaml b/s4/configs/experiment/audio/sashimi-dx7-vc-fir.yaml index 1e8017f..08505a9 100644 --- a/s4/configs/experiment/audio/sashimi-dx7-vc-fir.yaml +++ b/s4/configs/experiment/audio/sashimi-dx7-vc-fir.yaml @@ -42,6 +42,8 @@ trainer: accumulate_grad_batches: 2 dataset: - sr: 8000 - duration: 2.5 - bit_rate: 8 \ No newline at end of file + data_module: + _target_: SingleVoice2VoiceDataModule + sr: 8000 + duration: 2.5 + bit_rate: 8 \ No newline at end of file diff --git a/s4/src/dataloaders/audio.py b/s4/src/dataloaders/audio.py index 5d60c9d..cb7093a 100644 --- a/s4/src/dataloaders/audio.py +++ b/s4/src/dataloaders/audio.py @@ -3,6 +3,7 @@ import os from os import listdir from os.path import join +import hydra import torch import torchaudio @@ -1090,22 +1091,7 @@ def init_defaults(self): def setup(self): - from s4_dx7.lightning.data.single_voice_to_voice import SingleVoice2VoiceDataModule - - self.dataset = SingleVoice2VoiceDataModule( - - bit_rate=self.bits, - sr=self.sr, - duration=self.duration, - limit=self.limit - # path=self.data_dir, - # bits=self.bits, - # split='train', - # quantization=self.quantization, - # dequantize=self.dequantize, - # pad_len=self.pad_len, - ) - + self.dataset = hydra.utils.instantiate(self.data_module) self.sample_len = int(self.duration*self.sr) def train_dataloader(self, **kwargs): return self.dataset.get_train_dataloader(kwargs["batch_size"]) diff --git a/s4_dx7/lib/layers/mamba2.py b/s4_dx7/lib/layers/mamba2.py index 5f2eb65..2bfff88 100644 --- a/s4_dx7/lib/layers/mamba2.py +++ b/s4_dx7/lib/layers/mamba2.py @@ -22,19 +22,13 @@ class Mamba2(nn.Module): def __init__( self, d_model, - d_state=64, - headdim=8, - A_init_range=(1.,1.1), **kwargs ): super().__init__() - self.layer = Mamba( # This module uses roughly 3 * expand * d_model^2 parameters d_model=d_model, # Model dimension d_model - d_state=d_state, # SSM state expansion factor - A_init_range=A_init_range, - headdim=headdim # https://github.com/state-spaces/mamba/issues/351#issuecomment-2167091940 + **kwargs # expand=6, ).to("cuda") diff --git a/s4_dx7/lightning/data/multi_voice_to_voice.py b/s4_dx7/lightning/data/multi_voice_to_voice.py index ceaf860..f0c5a45 100644 --- a/s4_dx7/lightning/data/multi_voice_to_voice.py +++ b/s4_dx7/lightning/data/multi_voice_to_voice.py @@ -12,7 +12,7 @@ from s4_dx7.utils import get_duckdb_connection def create_connection(): - # duckdb.execute('install sqlite') + duckdb.execute('install sqlite') duck = duckdb.connect(os.environ['AUDIO_DB']) Plugin.configure_connection(None, duck) @@ -55,6 +55,7 @@ def _dataset(self, data_split): order by rowid {limit_str} ''', create_connection, + override_num_blocks=1 ) if data_split == DataSplit.TRAIN: ds = ds.random_shuffle() @@ -65,7 +66,13 @@ def _dataset(self, data_split): duration=self.duration, bit_rate=self.bit_rate, baud_rate=self.patch_baud_rate) - pipeline = ds.map_batches(f, zero_copy_batch=True, batch_size=5) + pipeline = ds.repartition(10).map_batches( + f, + zero_copy_batch=True, + batch_size=16, + num_cpus=1, + concurrency=1 + ) # raise ValueError(pipeline.stats()) return pipeline @@ -109,9 +116,11 @@ def f(batch, table, sr, duration, bit_rate, baud_rate): signals_target = mu_law_encoding(signals_target, bit_rate) signals_source = mu_law_encoding(signals_source, bit_rate) + # raise ValueError(signals_source.shape) + # raise ValueError(signals_source.shape, signals_target.shape) return { - "x": signals_source[:,:-1], - "y": signals_target[:,:-1].unsqueeze(-1), + "x": signals_source, + "y": signals_target, "encoding": target_encoding, # "source_voice_id": batch_table['source_voice_id'].to_pylist(), # "target_voice_id": batch_table['target_voice_id'].to_pylist(), From 31a6df4019fdf76dfb14b0f33389a9411d2c30b0 Mon Sep 17 00:00:00 2001 From: Nintorac Dev Date: Tue, 17 Sep 2024 06:08:00 +0000 Subject: [PATCH 11/11] [feat] add performance eval notebook --- .gitignore | 3 +- notebooks/dataloaders/multi_voice2voice.py | 25 +++++- .../dataloaders/multi_voice2voice_perf.py | 70 +++++++++++++++ poetry.lock | 89 +++++++++---------- pyproject.toml | 1 + s4_dx7/lightning/data/multi_voice_to_voice.py | 47 +++++++--- s4_dx7/udf/render.py | 19 ++++ 7 files changed, 196 insertions(+), 58 deletions(-) create mode 100644 notebooks/dataloaders/multi_voice2voice_perf.py diff --git a/.gitignore b/.gitignore index 52f600f..521f9e2 100644 --- a/.gitignore +++ b/.gitignore @@ -35,4 +35,5 @@ experiments/ .obsidian/ -*.ipynb \ No newline at end of file +*.ipynb +/ray/ \ No newline at end of file diff --git a/notebooks/dataloaders/multi_voice2voice.py b/notebooks/dataloaders/multi_voice2voice.py index 43707f9..3fcdcbc 100644 --- a/notebooks/dataloaders/multi_voice2voice.py +++ b/notebooks/dataloaders/multi_voice2voice.py @@ -25,7 +25,7 @@ bit_rate=8 sr=40000 baud=8000 -sample_size=15 +sample_size=100 dt = int(1/baud*sr) # samples per bit encoding_duration = (dt*8*155)/sr @@ -33,7 +33,10 @@ data_module = MultiVoice2VoiceDataModule(bit_rate=bit_rate, limit=sample_size, sr=sr, patch_baud_rate=baud) # Iterate over each batch (assuming a single batch here for simplicity) -for batch in data_module.get_train_dataloader(sample_size): +loader = data_module.get_train_dataloader(sample_size) + +#%% +for batch in loader: batch_size = batch['x'].shape[0] for i in range(5): @@ -96,3 +99,21 @@ break # %% +for i in loader: + pass +# %% +print(loader.stats()) +# %% +pipeline = data_module._dataset('train') +# %% +dir(pipeline) +# %% +pipeline.take_batch() +# %% +print(pipeline.stats()) +# %% +for i in pipeline.iter_torch_batches(batch_size=14): + pass +# %% +print(pipeline.stats()) +# %% diff --git a/notebooks/dataloaders/multi_voice2voice_perf.py b/notebooks/dataloaders/multi_voice2voice_perf.py new file mode 100644 index 0000000..a35557d --- /dev/null +++ b/notebooks/dataloaders/multi_voice2voice_perf.py @@ -0,0 +1,70 @@ +# %% +from datetime import datetime +from time import sleep +import torch +import librosa +import librosa.display +import matplotlib.pyplot as plt +from IPython.display import Audio, display +import numpy as np +import pandas as pd +import scipy.signal as s +from s4_dx7.lightning.data import MultiVoice2VoiceDataModule +from s4_dx7.lib.visualistaion.audio import waveform_segment_figure +from torchaudio.functional import mu_law_decoding +from s4_dx7.lightning.data.multi_voice_to_voice import PipelineConfiguration +from s4_dx7.notebook.mel_spec_audio import plot_melspectrogram_and_play_button +from tqdm import tqdm_notebook +## TODO figure out how to remove error ouput from rendered document +import ray +#%% +ray.init(_temp_dir='/app/ray') +#%% +for i in tqdm_notebook(range(3)): pass +#%% +# Define a helper function to plot the mel spectrogram and create the play button +bit_rate=8 +sr=40000 +baud=8000 +sample_size=10000 +batch_size=2 + +dt = int(1/baud*sr) # samples per bit +encoding_duration = (dt*8*155)/sr +encoding_samples = dt*8*155 +pipe_config = PipelineConfiguration( + f_batch_size=None, + partitions=5000, + f_concurrency=3, + override_read_blocks=1, + f_num_cpus=1, + loader_prefetch = None +) + + +data_module = MultiVoice2VoiceDataModule( + bit_rate=bit_rate, + limit=sample_size, + sr=sr, + patch_baud_rate=baud, + pipeline_config=pipe_config +) +# %% +pipeline = data_module._dataset('train') +# %% +# %% +times = [datetime.now().timestamp()] +for i, x in tqdm_notebook(enumerate(pipeline.iter_torch_batches(batch_size=batch_size))): + sleep(0.5) # model.forward().backwared() + times.append(datetime.now().timestamp()) + + print(len(x['rowid'])) + if not i % 1000: + plt.scatter((range(len(times))),np.gradient(np.array(times))) + plt.show() + plt.close('all') + pass +# %% +print(pipeline.stats()) +# %% +# %% diff --git a/poetry.lock b/poetry.lock index 574ae7f..b7b267f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1768,6 +1768,27 @@ qtconsole = ["qtconsole"] test = ["packaging", "pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"] test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"] +[[package]] +name = "ipywidgets" +version = "8.1.5" +description = "Jupyter interactive widgets" +optional = false +python-versions = ">=3.7" +files = [ + {file = "ipywidgets-8.1.5-py3-none-any.whl", hash = "sha256:3290f526f87ae6e77655555baba4f36681c555b8bdbbff430b70e52c34c86245"}, + {file = "ipywidgets-8.1.5.tar.gz", hash = "sha256:870e43b1a35656a80c18c9503bbf2d16802db1cb487eec6fab27d683381dde17"}, +] + +[package.dependencies] +comm = ">=0.1.3" +ipython = ">=6.1.0" +jupyterlab-widgets = ">=3.0.12,<3.1.0" +traitlets = ">=4.3.1" +widgetsnbextension = ">=4.0.12,<4.1.0" + +[package.extras] +test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"] + [[package]] name = "isodate" version = "0.6.1" @@ -1933,6 +1954,17 @@ files = [ {file = 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(>=1.0.0)"] -ssh = ["paramiko"] - -[[package]] -name = "ply" -version = "3.11" -description = "Python Lex & Yacc" -optional = false -python-versions = "*" -files = [ - {file = "ply-3.11-py2.py3-none-any.whl", hash = "sha256:096f9b8350b65ebd2fd1346b12452efe5b9607f7482813ffca50c22722a807ce"}, - {file = "ply-3.11.tar.gz", hash = "sha256:00c7c1aaa88358b9c765b6d3000c6eec0ba42abca5351b095321aef446081da3"}, -] - [[package]] name = "pooch" version = "1.8.2" @@ -5931,6 +5919,17 @@ files = [ {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"}, ] +[[package]] +name = "widgetsnbextension" +version = "4.0.13" +description = "Jupyter interactive widgets for Jupyter Notebook" +optional = false +python-versions = ">=3.7" +files = [ + {file = "widgetsnbextension-4.0.13-py3-none-any.whl", hash = "sha256:74b2692e8500525cc38c2b877236ba51d34541e6385eeed5aec15a70f88a6c71"}, + {file = "widgetsnbextension-4.0.13.tar.gz", hash = "sha256:ffcb67bc9febd10234a362795f643927f4e0c05d9342c727b65d2384f8feacb6"}, +] + [[package]] name = "wrapt" version = "1.16.0" @@ -6269,4 +6268,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = "^3.11" -content-hash = "41d777a1a10ca97913bff2f543ee6e46db1a10b2805abc719dadca40a85f46d1" +content-hash = "005130da72d7bbc3fe107ebe3fb4fb17cea42cb672c6b5747897c446b071a983" diff --git a/pyproject.toml b/pyproject.toml index 931f08f..e3dab1d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -54,6 +54,7 @@ ipykernel = "^6.28.0" pyvis = "^0.3.2" jupytext = "^1.16.4" nbconvert = "^7.16.4" +ipywidgets = "^8.1.5" [tool.poetry.group.debug.dependencies] diff --git a/s4_dx7/lightning/data/multi_voice_to_voice.py b/s4_dx7/lightning/data/multi_voice_to_voice.py index f0c5a45..dbf30c1 100644 --- a/s4_dx7/lightning/data/multi_voice_to_voice.py +++ b/s4_dx7/lightning/data/multi_voice_to_voice.py @@ -1,8 +1,10 @@ from enum import StrEnum from functools import partial import os +from typing import Optional import duckdb import pyarrow as pa +from pydantic import BaseModel import ray import numpy as np import torch @@ -10,6 +12,10 @@ from s4_dx7.lib.render import render_batch from s4_dx7.udf import Plugin from s4_dx7.utils import get_duckdb_connection +import ray + +ctx = ray.data.DataContext.get_current() +ctx.use_push_based_shuffle = True def create_connection(): duckdb.execute('install sqlite') @@ -23,6 +29,14 @@ class DataSplit(StrEnum): TEST = 'test' VALIDATE = 'validate' +class PipelineConfiguration(BaseModel): + partitions: Optional[int] = 10 + f_batch_size: Optional[int] = 16 + f_concurrency: Optional[int] = 4 + override_read_blocks: Optional[int] = None + f_num_cpus: int = 1 + loader_prefetch: Optional[int] = None + class MultiVoice2VoiceDataModule(): def __init__(self, table='multivoice_dataset', @@ -30,10 +44,13 @@ def __init__(self, sr=44100, duration=2.5, limit=None, - patch_baud_rate=300 - + patch_baud_rate=300, + pipeline_config: PipelineConfiguration = None ): print(bit_rate, sr, duration) + + if pipeline_config is None: pipeline_config = PipelineConfiguration() + self.pipeline_config = pipeline_config self.bit_rate = bit_rate self.duration = duration self.sr = sr @@ -43,6 +60,7 @@ def __init__(self, self._tl = None self._el = None self._tel = None + def _dataset(self, data_split): limit_str = '' if self.limit is None else f'limit {self.limit}' @@ -55,10 +73,9 @@ def _dataset(self, data_split): order by rowid {limit_str} ''', create_connection, - override_num_blocks=1 + override_num_blocks=self.pipeline_config.override_read_blocks ) - if data_split == DataSplit.TRAIN: - ds = ds.random_shuffle() + f = partial(self.f, table=self.table, @@ -66,21 +83,30 @@ def _dataset(self, data_split): duration=self.duration, bit_rate=self.bit_rate, baud_rate=self.patch_baud_rate) - pipeline = ds.repartition(10).map_batches( + if data_split == DataSplit.TRAIN: + ds = ds.random_shuffle() + if self.pipeline_config.partitions: + ds = ds.repartition(self.pipeline_config.partitions, shuffle=True) + + pipeline = ds.map_batches( f, zero_copy_batch=True, - batch_size=16, - num_cpus=1, - concurrency=1 + batch_format='pandas', + batch_size=self.pipeline_config.f_batch_size, + num_cpus=self.pipeline_config.f_num_cpus, + concurrency=self.pipeline_config.f_concurrency ) # raise ValueError(pipeline.stats()) return pipeline + # return ds + # self._dataset = dataset @staticmethod def f(batch, table, sr, duration, bit_rate, baud_rate): conn = create_connection() + # raise ValueError(batch) batch_table = conn.execute(f''' with batch_data as ( select * from "{table}" @@ -123,6 +149,7 @@ def f(batch, table, sr, duration, bit_rate, baud_rate): "y": signals_target, "encoding": target_encoding, # "source_voice_id": batch_table['source_voice_id'].to_pylist(), + "rowid": batch.rowid # "target_voice_id": batch_table['target_voice_id'].to_pylist(), # "phrase_id": batch_table['phrase_id'].to_pylist(), } @@ -132,7 +159,7 @@ def get_train_dataloader(self, batch_size): if self._tl is None: self._tl = self._dataset('train').iter_torch_batches( batch_size=batch_size, - # prefetch_batches=2 + prefetch_batches=self.pipeline_config.loader_prefetch ) return self._tl diff --git a/s4_dx7/udf/render.py b/s4_dx7/udf/render.py index 4582549..261f4ca 100644 --- a/s4_dx7/udf/render.py +++ b/s4_dx7/udf/render.py @@ -49,3 +49,22 @@ def render_dx7_udf( # return pa.array(ray.get(promises)) results = ray.get(promises) return pa.concat_arrays(results) + + +# def render_dx7_udf( +# notes: VARCHAR, +# voice: BLOB, +# sr: INTEGER, +# duration: FLOAT +# )->AUDIO_TYPE: +# """ +# probably not super performant at scale but good enough for training +# """ +# results = [] +# a = lambda x: pyarrow.array([x]) +# for n, v, s, d in zip(notes, voice, sr, duration): +# promise = render_batch(a(n), s.as_py(), d.as_py(), v.as_py(), pcm_encode=False) +# results.append(promise) + +# # return pa.array(ray.get(promises)) +# return pa.concat_arrays(results)