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# Copyright 2024 The YourMT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Please see the details in the LICENSE file.
""" tokenizer.py: Encodes and decodes events to/from tokens. """
import numpy as np
import warnings
from abc import ABC, abstractmethod
from utils.note_event_dataclasses import Event, EventRange, Note #, Codec
from utils.event_codec import FastCodec as Codec
from utils.note_event_dataclasses import NoteEvent
from utils.note2event import note_event2event
from utils.event2note import event2note_event, note_event2note
from typing import List, Optional, Union, Tuple, Dict, Counter
#TODO: Too complex to be an abstract class.
class EventTokenizerBase(ABC):
"""
A base class for encoding and decoding events to and from tokens.
"""
def __init__(
self,
base_codec: Union[Codec, str] = 'mt3',
special_tokens: List[str] = ['PAD', 'EOS', 'UNK'],
extra_tokens: List[str] = [],
max_shift_steps: int = 206, # 1001 in Gardner et al.
program_vocabulary: Optional[Dict] = None,
drum_vocabulary: Optional[Dict] = None,
) -> None:
"""
Initializes the EventTokenizerBase object.
:param base_codec: The codec to use for encoding and decoding.
:param special_tokens: None or list of special tokens to include in the vocabulary.
:param extra_tokens: None or list of tokens to be treated as additional special tokens.
:param program_vocabulary: None or a dictionary mapping program names to program indices.
:param drum_vocabulary: None or a dictionary mapping drum names to drum indices.
:param max_shift_steps: The maximum number of shift steps to use for the codec.
"""
# Initialize the codec attribute based on the input codec parameter.
if isinstance(base_codec, str):
# If codec is a string, initialize codec with the appropriate Codec object.
if base_codec.lower() == 'mt3':
event_ranges = [
EventRange('pitch', min_value=0, max_value=127),
EventRange('velocity', min_value=0, max_value=1),
EventRange('tie', min_value=0, max_value=0),
EventRange('program', min_value=0, max_value=127),
EventRange('drum', min_value=0, max_value=127),
]
else:
raise ValueError(f'Unknown codec name: {base_codec}')
# Initialize codec
self.codec = Codec(special_tokens=special_tokens + extra_tokens,
max_shift_steps=max_shift_steps,
event_ranges=event_ranges,
program_vocabulary=program_vocabulary,
drum_vocabulary=drum_vocabulary,
name='mt3')
elif isinstance(base_codec, Codec):
# If codec is a Codec object, store it directly.
self.codec = base_codec
if program_vocabulary is not None or drum_vocabulary is not None:
print('')
warnings.warn("Vocabulary cannot be applied when using a custom codec.")
else:
# If codec is neither a string nor a Codec object, raise a NotImplementedError.
raise TypeError(f'Unknown codec type: {type(base_codec)}')
self.num_tokens = self.codec._num_classes
def _encode(self, events: List[Event]) -> List[int]:
return [self.codec.encode_event(e) for e in events]
def _decode(self, tokens: List[int]) -> List[Event]:
return [self.codec.decode_event_index(idx) for idx in tokens]
@abstractmethod
def encode(self):
""" Encode your custom events to tokens. """
pass
@abstractmethod
def decode(self):
""" Decode your custom tokens to events."""
pass
class EventTokenizer(EventTokenizerBase):
"""
Eencoding and decoding events to and from tokens.
"""
def __init__(self,
base_codec: Union[Codec, str] = 'mt3',
special_tokens: List[str] = ['PAD', 'EOS', 'UNK'],
extra_tokens: List[str] = [],
max_shift_steps: int = 206,
program_vocabulary: Optional[Dict] = None,
drum_vocabulary: Optional[Dict] = None) -> None:
"""
Initializes the EventTokenizerBase object.
:param codec: The codec to use for encoding and decoding.
:param special_tokens: None or list of special tokens to include in the vocabulary.
:param extra_tokens: None or list of tokens to be treated as additional special tokens.
:param program_vocabulary: None or a dictionary mapping program names to program indices.
:param drum_vocabulary: None or a dictionary mapping drum names to drum indices.
:param max_shift_steps: The maximum number of shift steps to use for the codec.
"""
# Initialize the codec attribute based on the input codec parameter.
super().__init__(
base_codec=base_codec,
special_tokens=special_tokens,
extra_tokens=extra_tokens,
max_shift_steps=max_shift_steps,
program_vocabulary=program_vocabulary,
drum_vocabulary=drum_vocabulary,
)
def encode(self, events):
""" Encode your custom events to tokens. """
return super()._encode(events)
def decode(self, tokens):
""" Decode your custom tokens to events."""
return super()._decode(tokens)
class NoteEventTokenizer(EventTokenizerBase):
""" Encodes and decodes note events to/from tokens. """
def __init__(
self,
base_codec: Union[Codec, str] = 'mt3',
max_length: int = 1024, # max length of tokens
tps: int = 100,
sort_note_event: bool = True,
special_tokens: List[str] = ['PAD', 'EOS', 'UNK'],
extra_tokens: List[str] = [],
max_shift_steps: int = 206,
program_vocabulary: Optional[Dict] = None,
drum_vocabulary: Optional[Dict] = None,
ignore_decoding_tokens: List[str] = [],
ignore_decoding_tokens_from_and_to: Optional[List[str]] = None,
debug_mode: bool = False) -> None:
"""
Initializes the TaskEventNoteTokenizer object.
List[NoteEvent] -> encdoe_note_events -> np.ndarray[int]
np.ndarray[int] -> decode_note_events -> Tuple[List[NoteEvent], List[NoteEvent]]
:param codec: The codec to use for encoding and decoding.
:param special_tokens: None or list of special tokens to include in the vocabulary.
:param extra_tokens: None or list of tokens to be treated as additional special tokens.
:param program_vocabulary: None or a dictionary mapping program names to program indices.
:param drum_vocabulary: None or a dictionary mapping drum names to drum indices.
:param max_shift_steps: The maximum number of shift steps to use for the codec.
:param ignore_decoding_tokens: List of tokens to ignore during decoding.
:param ignore_decoding_tokens_from_and_to: List of tokens to ignore during decoding. [from, to]
"""
super().__init__(base_codec=base_codec,
special_tokens=special_tokens,
extra_tokens=extra_tokens,
max_shift_steps=max_shift_steps,
program_vocabulary=program_vocabulary,
drum_vocabulary=drum_vocabulary)
self.max_length = max_length
self.tps = tps
self.sort = sort_note_event
# Prepare prefix, suffix and pad tokens.
self._prefix = []
self._suffix = []
for stk in self.codec.special_tokens:
if stk == 'EOS':
self._suffix.append(self.codec.special_tokens.index('EOS'))
elif stk == 'PAD':
self._zero_pad = [0] * 1024
elif stk == 'UNK':
pass
else:
pass
# raise NotImplementedError(f'Unknown special token: {stk}')
self.eos_id = self.codec.special_tokens.index('EOS')
self.pad_id = self.codec.special_tokens.index('PAD')
self.ids_to_ignore_decoding = [self.codec.special_tokens.index(t) for t in ignore_decoding_tokens]
self.ignore_tokens_from_and_to = ignore_decoding_tokens_from_and_to
self.debug_mode = debug_mode
def _decode(self, tokens):
# This is event detokenizer, not note_event. It is required for displaying events in validation dashboard
return super()._decode(tokens)
def encode(
self,
note_events: List[NoteEvent],
tie_note_events: Optional[List[NoteEvent]] = None,
start_time: float = 0.,
) -> List[int]:
""" Encodes note events and tie note events to tokens. """
events = note_event2event(
note_events=note_events,
tie_note_events=tie_note_events,
start_time=start_time, # required for calcuating relative time
tps=self.tps,
sort=self.sort)
return super()._encode(events)
def encode_plus(
self,
note_events: List[NoteEvent],
tie_note_events: Optional[List[NoteEvent]] = None,
start_times: float = 0., # Fixing bug: start_time --> start_times
add_special_tokens: Optional[bool] = True,
max_length: Optional[int] = None, # if None, use self.max_length
pad_to_max_length: Optional[bool] = True,
return_attention_mask: bool = False) -> Union[List[int], Tuple[List[int], List[int]]]:
""" Encodes note events and tie note info to padded tokens. """
encoded = self.encode(note_events, tie_note_events, start_times)
# if task_events:
# encoded = super()._encode(task_events) + encoded
if add_special_tokens:
if self._prefix:
encoded = self._prefix + encoded
if self._suffix:
encoded = encoded + self._suffix
if max_length is None:
max_length = self.max_length
length = len(encoded)
if length >= max_length:
encoded = encoded[:max_length]
length = max_length
if return_attention_mask:
attention_mask = [1] * length
# <PAD>
if pad_to_max_length is True:
if len(self._zero_pad) != max_length:
self._zero_pad = [self.pad_id] * max_length
if return_attention_mask:
attention_mask += self._zero_pad[length:]
encoded = encoded + self._zero_pad[length:]
if return_attention_mask:
return encoded, attention_mask
return encoded
def encode_task(self, task_events: List[Event], max_length: Optional[int] = None) -> List[int]:
# NOTE: This is an event tokenizer that generates task ids, not the list of note_event objects.
encoded = super()._encode(task_events)
# <PAD>
if max_length is not None:
if len(self._zero_pad_task) != max_length:
self._zero_pad_task = [self.pad_id] * max_length
length = len(encoded)
encoded = encoded + self._zero_pad[length:]
return encoded
def decode(
self,
tokens: List[int],
start_time: float = 0.,
return_events: bool = False,
) -> Union[Tuple[List[NoteEvent], List[NoteEvent]], Tuple[List[NoteEvent], List[NoteEvent], List[Tuple[int]],
List[Event], int]]:
"""Decodes a sequence of tokens into note events.
Args:
tokens (List[int]): The list of tokens to be decoded.
start_time (float, optional): The starting time for the note events. Defaults to 0.
return_events (bool, optional): Indicates whether to include the raw events in the return value.
Defaults to False.
Returns:
Union[Tuple[List[NoteEvent], List[NoteEvent]],
Tuple[List[NoteEvent], List[NoteEvent], List[Event], int]]: The decoded note events.
If `return_events` is False, the returned tuple contains `note_events`, `tie_note_events`,
`last_activity`, and `err_cnt`.
If `return_events` is True, the returned tuple contains `note_events`, `tie_note_events`,
`last_activity`, `events`, and `err_cnt`.
"""
if self.debug_mode:
ignored_tokens_from_input = [t for t in tokens if t in self.ids_to_ignore_decoding]
print(ignored_tokens_from_input)
if self.ids_to_ignore_decoding:
tokens = [t for t in tokens if t not in self.ids_to_ignore_decoding]
events = super()._decode(tokens)
note_events, tie_note_events, last_activity, err_cnt = event2note_event(events, start_time, True, self.tps)
if return_events:
return note_events, tie_note_events, last_activity, events, err_cnt
else:
return note_events, tie_note_events, last_activity, err_cnt
def decode_batch(
self,
batch_tokens: Union[List[List[int]], np.ndarray],
start_times: List[float],
return_events: bool = False
) -> Union[Tuple[List[Tuple[List[NoteEvent], List[NoteEvent], List[Tuple[int]], List[float]]], int],
Tuple[List[Tuple[List[NoteEvent], List[NoteEvent], List[Tuple[int]], List[float]]], List[List[Event]],
Counter[str]]]:
"""
Decodes a batch of tokens to note_events and tie_note_events.
Args:
batch_tokens (List[List[int]] or np.ndarray): Tokens to be decoded.
start_times (List[float]): List of start times for each token set.
return_events (bool, optional): Flag to determine if events should be returned. Defaults to False.
"""
if isinstance(batch_tokens, np.ndarray):
batch_tokens = batch_tokens.tolist()
if len(batch_tokens) != len(start_times):
raise ValueError('The length of batch_tokens and start_times must be same.')
zipped_note_events_and_tie = []
list_events = []
total_err_cnt = 0
for tokens, start_time in zip(batch_tokens, start_times):
if return_events:
note_events, tie_note_events, last_activity, events, err_cnt = self.decode(
tokens, start_time, return_events)
list_events.append(events)
else:
note_events, tie_note_events, last_activity, err_cnt = self.decode(tokens, start_time, return_events)
zipped_note_events_and_tie.append((note_events, tie_note_events, last_activity, start_time))
total_err_cnt += err_cnt
if return_events:
return zipped_note_events_and_tie, list_events, total_err_cnt
else:
return zipped_note_events_and_tie, total_err_cnt
def decode_list_batches(
self,
list_batch_tokens: Union[List[List[List[int]]], List[np.ndarray]],
list_start_times: Union[List[List[float]], List[float]],
return_events: bool = False
) -> Union[Tuple[List[List[Tuple[List[NoteEvent], List[NoteEvent], List[Tuple[int]], List[float]]]], Counter[str]],
Tuple[List[List[Tuple[List[NoteEvent], List[NoteEvent], List[Tuple[int]], List[float]]]],
List[List[Event]], Counter[str]]]:
"""
Decodes a list of variable-size batches of token array to a list of
zipped note_events and tie_note_events.
Args:
list_batch_tokens: List[np.ndarray], where array shape is (batch_size, variable_length)
list_start_times: List[float], where the length is sum of all batch_sizes.
return_events: bool, Defaults to False.
Returns:
list_list_zipped_note_events_and_tie:
List[
Tuple[
List[NoteEvent]: A list of note events.
List[NoteEvent]: A list of tie note events.
List[Tuple[int]]: A list of last activity of segment. [(program, pitch), ...]. This is useful
for validating notes within a batch of segments extracted from a file.
List[float]: A list of segment start times.
]
]
(Optional) list_events:
List[List[Event]]
total_err_cnt:
Counter[str]: error counter.
"""
list_tokens = []
for arr in list_batch_tokens:
for tokens in arr:
list_tokens.append(tokens)
assert (len(list_tokens) == len(list_start_times))
zipped_note_events_and_tie = []
list_events = []
total_err_cnt = Counter()
for tokens, start_time in zip(list_tokens, list_start_times):
note_events, tie_note_events, last_activity, events, err_cnt = self.decode(
tokens, start_time, return_events)
zipped_note_events_and_tie.append((note_events, tie_note_events, last_activity, start_time))
if return_events:
list_events.append(events)
total_err_cnt += err_cnt
if return_events:
return zipped_note_events_and_tie, list_events, total_err_cnt
else:
return zipped_note_events_and_tie, total_err_cnt
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