From cec0f348077f27c513e32e86e69e902d3362b8c5 Mon Sep 17 00:00:00 2001 From: Dvid Noel Ng Date: Tue, 10 Dec 2024 19:51:29 +0100 Subject: [PATCH] Remove espeak --- demo.ipynb | 16 +- glados/phonemizer.py | 295 +++++++++++++++++++++++++++++++++++ glados/tts.py | 42 +++-- models/idx_to_token.pkl | Bin 0 -> 447 bytes models/lang_phoneme_dict.pkl | Bin 0 -> 8673468 bytes models/phoneme_to_id.pkl | Bin 0 -> 1537 bytes models/token_to_idx.pkl | Bin 0 -> 356 bytes 7 files changed, 322 insertions(+), 31 deletions(-) create mode 100644 glados/phonemizer.py create mode 100644 models/idx_to_token.pkl create mode 100644 models/lang_phoneme_dict.pkl create mode 100644 models/phoneme_to_id.pkl create mode 100644 models/token_to_idx.pkl diff --git a/demo.ipynb b/demo.ipynb index 2a971ec..78cfdad 100644 --- a/demo.ipynb +++ b/demo.ipynb @@ -23,8 +23,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Instantiate the TTS engine\n", - "glados_tts = tts.TTSEngine()" + "glados_tts = tts.Synthesizer(\n", + " model_path=str(\"models/glados.onnx\"),\n", + " use_cuda=True,\n", + " speaker_id=0,\n", + " )" ] }, { @@ -34,11 +37,10 @@ "outputs": [], "source": [ "# Generate the audio.\n", - "# Glados is spelt incorrectly on purpose to make the pronunciation more accurate.\n", - "audio = glados_tts.generate_speech_audio(\"Hello, my name is Gladohs. I am an AI created by Aperture Science.\")\n", + "audio = glados_tts.generate_speech_audio(\"Hello, my name is Glados. I am an AI created by Aperture Science.\")\n", "\n", "# Play the audio\n", - "sd.play(audio, tts.RATE)" + "sd.play(audio, glados_tts.rate)" ] }, { @@ -88,7 +90,7 @@ ], "metadata": { "kernelspec": { - "display_name": "GLaDOS", + "display_name": "glados", "language": "python", "name": "python3" }, @@ -102,7 +104,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.10.15" } }, "nbformat": 4, diff --git a/glados/phonemizer.py b/glados/phonemizer.py new file mode 100644 index 0000000..a476ebd --- /dev/null +++ b/glados/phonemizer.py @@ -0,0 +1,295 @@ +from pickle import load +from typing import List, Iterable, Dict, Union +from functools import cache +from pathlib import Path +import re +from itertools import zip_longest + +import numpy as np +import onnxruntime +from enum import Enum + +class ModelConfig: + MODEL_NAME: str = 'models/phomenizer_en.onnx' + PHONEME_DICT_PATH: Path = Path('./models/lang_phoneme_dict.pkl') + TOKEN_TO_IDX_PATH: Path = Path('./models/token_to_idx.pkl') + IDX_TO_TOKEN_PATH: Path = Path('./models/idx_to_token.pkl') + CHAR_REPEATS: int = 3 + MODEL_INPUT_LENGTH: int = 64 + EXPAND_ACRONYMS: bool = True + USE_CUDA: bool = True + +class SpecialTokens(Enum): + PAD = '_' + START = '' + END = '' + EN_US = '' + +class Punctuation(Enum): + PUNCTUATION = '().,:?!/–' + HYPHEN = '-' + SPACE = ' ' + + @classmethod + @cache + def get_punc_set(cls) -> set[str]: + return set(cls.PUNCTUATION.value + cls.HYPHEN.value + cls.SPACE.value) + + @classmethod + @cache + def get_punc_pattern(cls) -> re.Pattern: + return re.compile(f'([{cls.PUNCTUATION.value + cls.SPACE.value}])') + +class Phonemizer: + def __init__(self) -> None: + # self.ort_session = onnxruntime.InferenceSession(ModelConfig.MODEL_NAME) + + + self.phoneme_dict = self._load_pickle(ModelConfig.PHONEME_DICT_PATH) + self.token_to_idx = self._load_pickle(ModelConfig.TOKEN_TO_IDX_PATH) + self.idx_to_token = self._load_pickle(ModelConfig.IDX_TO_TOKEN_PATH) + + providers = ["CPUExecutionProvider"] + if ModelConfig.USE_CUDA: + providers = [ + ("CUDAExecutionProvider", {"cudnn_conv_algo_search": "HEURISTIC"}), + "CPUExecutionProvider", + ] + + self.ort_session = onnxruntime.InferenceSession( + ModelConfig.MODEL_NAME, + sess_options=onnxruntime.SessionOptions(), + providers=providers, + ) + + + self.special_tokens: set[str] = {SpecialTokens.PAD.value, SpecialTokens.END.value, SpecialTokens.EN_US.value} + + @staticmethod + def _load_pickle(path: Path) -> dict: + """Load a pickled dictionary from path.""" + with path.open('rb') as f: + return load(f) + + @staticmethod + def unique_consecutive(arr: np.ndarray) -> List[np.ndarray]: + """ + Equivalent to torch.unique_consecutive for numpy arrays. + + :param arr: Array to process. + :return: Array with consecutive duplicates removed. + """ + result = [] + for row in arr: + if len(row) == 0: + result.append(row) + else: + mask = np.concatenate(([True], row[1:] != row[:-1])) + result.append(row[mask]) + return result + + @staticmethod + def remove_padding(arr: np.ndarray, padding_value: int = 0) -> List[np.ndarray]: + return [row[row != padding_value] for row in arr] + + @staticmethod + def trim_to_stop(arr: np.ndarray, end_index: int = 2) -> List[np.ndarray]: + """ + Trims the array to the stop index. + + :param arr: Array to trim. + :param end_index: Index to stop at. + """ + result = [] + for row in arr: + stop_index = np.where(row == end_index)[0] + if len(stop_index) > 0: + result.append(row[:stop_index[0]+1]) + else: + result.append(row) + return result + + def process_model_output(self, arr: np.ndarray) -> List[np.ndarray]: + """ + Processes the output of the model to get the phoneme indices. + + :param arr: Model output. + :return: List of phoneme indices. + """ + + arr = np.argmax(arr[0], axis=2) + arr = self.unique_consecutive(arr) + arr = self.remove_padding(arr) + arr = self.trim_to_stop(arr) + return arr + + @staticmethod + def expand_acronym(word: str) -> str: + """ + Expands an acronym into its subwords. + + :param word: Acronym to expand. + :return: Expanded acronym. + """ + subwords = [] + for subword in word.split(Punctuation.HYPHEN.value): + expanded = [] + for a, b in zip_longest(subword, subword[1:]): + expanded.append(a) + if b is not None and b.isupper(): + expanded.append(Punctuation.HYPHEN.value) + expanded = ''.join(expanded) + subwords.append(expanded) + return Punctuation.HYPHEN.value.join(subwords) + + def encode(self, sentence: Iterable[str]) -> List[int]: + """ + Maps a sequence of symbols for a language to a sequence of indices. + + :param sentence: Sentence (or word) as a sequence of symbols. + :return: Sequence of token indices. + """ + sentence = [item for item in sentence for _ in range(ModelConfig.CHAR_REPEATS)] + sentence = [s.lower() for s in sentence] + sequence = [self.token_to_idx[c] for c in sentence if c in self.token_to_idx] + return [self.token_to_idx[SpecialTokens.START.value]] + sequence + [self.token_to_idx[SpecialTokens.END.value]] + + def decode(self, sequence: np.ndarray) -> str: + """ + Maps a sequence of indices to an array of symbols. + + :param sequence: Encoded sequence to be decoded. + :return: Decoded sequence of symbols. + """ + decoded = [self.idx_to_token[int(t)] for t in sequence if int(t) in self.idx_to_token] + return ''.join(d for d in decoded if d not in self.special_tokens) + + @staticmethod + def pad_sequence_fixed(v: List[np.ndarray], target_length: int = ModelConfig.MODEL_INPUT_LENGTH) -> np.ndarray: + """ + Pad or truncate a list of arrays to a fixed length. + + :param v: List of arrays. + :param target_length: Target length to pad or truncate to. + :return: Padded array. + """ + result = np.zeros((len(v), target_length), dtype=np.int64) + + for i, seq in enumerate(v): + length = min(len(seq), target_length) # Handle both shorter and longer sequences + result[i, :length] = seq[:length] # Copy either the full sequence or its truncated version + + return result + + + def _get_dict_entry(self, word: str, lang: str, punc_set: set[str]) -> str | None: + """ + Gets the phoneme entry for a word in the dictionary. + + :param word: Word to get phoneme entry for. + :param lang: Language of the word. + :param punc_set: Set of punctuation characters. + :return: Phoneme entry for the word. + """ + if word in punc_set or len(word) == 0: + return word + if not self.phoneme_dict or lang not in self.phoneme_dict: + return None + phoneme_dict = self.phoneme_dict[lang] + if word in phoneme_dict: + return phoneme_dict[word] + elif word.lower() in phoneme_dict: + return phoneme_dict[word.lower()] + elif word.title() in phoneme_dict: + return phoneme_dict[word.title()] + else: + return None + + @staticmethod + def _get_phonemes(word: str, + word_phonemes: Dict[str, Union[str, None]], + word_splits: Dict[str, List[str]]) -> str: + """ + Gets the phonemes for a word. If the word is not in the phoneme dictionary, it is split into subwords. + + :param word: Word to get phonemes for. + :param word_phonemes: Dictionary of word phonemes. + """ + phons = word_phonemes[word] + if phons is None: + subwords = word_splits[word] + subphons = [word_phonemes[w] for w in subwords] + phons = ''.join(subphons) + return phons + + def _clean_and_split_texts(self, texts: List[str], punc_set: set[str], punc_pattern: re.Pattern) -> tuple[List[List[str]], set[str]]: + split_text, cleaned_words = [], set() + for text in texts: + cleaned_text = ''.join(t for t in text if t.isalnum() or t in punc_set) + split = [s for s in re.split(punc_pattern, cleaned_text) if len(s) > 0] + split_text.append(split) + cleaned_words.update(split) + return split_text, cleaned_words + + def convert_to_phonemes(self, texts: List[str], lang: str) -> List[str]: + """ + Converts a list of texts to phonemes using a phonemizer. + + :param texts: List of texts to convert. + :param lang: Language of the texts. + :return: List of phonemes. + """ + split_text, cleaned_words = [], set() + punc_set = Punctuation.get_punc_set() + punc_pattern = Punctuation.get_punc_pattern() + + # Step 1: Preprocess texts + split_text, cleaned_words = self._clean_and_split_texts(texts, punc_set, punc_pattern) + + # Step 2: Collect dictionary phonemes for words and hyphenated words + for punct in punc_set: + self.phoneme_dict[punct] = punct + word_phonemes = {word: self.phoneme_dict.get(word) for word in cleaned_words} + + + # Step 3: If word is not in dictionary, split it into subwords + words_to_split = [w for w in cleaned_words if word_phonemes[w] is None] + + word_splits = { + key: re.split(r'([-])', self.expand_acronym(word) if ModelConfig.EXPAND_ACRONYMS else word) + for key, word in zip(words_to_split, words_to_split) + } + + subwords = {w for values in word_splits.values() for w in values if w not in word_phonemes} + + for subword in subwords: + word_phonemes[subword] = self._get_dict_entry(word=subword, lang=lang, punc_set=punc_set) + + # Step 4: Predict all subwords that are missing in the phoneme dict + words_to_predict = [word for word, phons in word_phonemes.items() + if phons is None and len(word_splits.get(word, [])) <= 1] + input_batch = [self.encode(word) for word in words_to_predict] + input_batch = self.pad_sequence_fixed(input_batch) + + + ort_inputs = {self.ort_session.get_inputs()[0].name: input_batch} + ort_outs = self.ort_session.run(None, ort_inputs) + + ids = self.process_model_output(ort_outs) + + # Step 5: Add predictions to the dictionary + for id, word in zip(ids, words_to_predict): + word_phonemes[word] = self.decode(id) + + # Step 6: Get phonemes for each word in the text + phoneme_lists = [] + for text in split_text: + text_phons = [ + self._get_phonemes(word=word, word_phonemes=word_phonemes, + word_splits=word_splits) + for word in text + ] + phoneme_lists.append(text_phons) + + return [''.join(phoneme_list) for phoneme_list in phoneme_lists] + diff --git a/glados/tts.py b/glados/tts.py index 5859d24..fc625f0 100644 --- a/glados/tts.py +++ b/glados/tts.py @@ -2,16 +2,21 @@ import re import subprocess from typing import Any, Dict, List, Mapping, Optional, Sequence +from pathlib import Path +from pickle import load import numpy as np import json import onnxruntime +from . import phonemizer + # Constants MAX_WAV_VALUE = 32767.0 # Settings MODEL_PATH = "./models/glados.onnx" +PHONEME_TO_ID_PATH = Path("./models/phoneme_to_id.pkl") USE_CUDA = True # Conversions @@ -261,7 +266,11 @@ def __init__( self, model_path: str, use_cuda: bool, speaker_id: Optional[int] = None ): self.session = self._initialize_session(model_path, use_cuda) - self.id_map = PHONEME_ID_MAP + self.phonemizer = phonemizer.Phonemizer() + # self.id_map = PHONEME_ID_MAP + + self.id_map = self._load_pickle(PHONEME_TO_ID_PATH) + try: # Load the configuration file config_file_path = model_path + ".json" @@ -287,6 +296,12 @@ def __init__( else None ) + @staticmethod + def _load_pickle(path: Path) -> dict: + """Load a pickled dictionary from path.""" + with path.open('rb') as f: + return load(f) + def _initialize_session( self, model_path: str, use_cuda: bool ) -> onnxruntime.InferenceSession: @@ -315,30 +330,9 @@ def generate_speech_audio(self, text: str) -> np.ndarray: def _phonemizer(self, input_text: str) -> List[str]: """Converts text to phonemes using espeak-ng.""" + phonemes = self.phonemizer.convert_to_phonemes([input_text], 'en_us') - try: - # Prepare the command to call espeak with the desired flags - command = [ - "espeak-ng", # 'C:\Program Files\eSpeak NG\espeak-ng.exe', - "--ipa=2", # Output phonemes in IPA format - "-q", # Quiet, no output except the phonemes - "--stdout", # Output the phonemes to stdout - input_text, - ] - - # Execute the command and capture the output - result = subprocess.run( - command, capture_output=True, text=True, check=True, encoding="utf-8" - ) - - phonemes = result.stdout.strip().replace("\n", ".").replace(" ", " ") - phonemes = re.sub(r"_+", "_", phonemes) - phonemes = re.sub(r"_ ", " ", phonemes) - return phonemes.splitlines() - - except subprocess.CalledProcessError as e: - print("Error in phonemization:", str(e)) - return [] + return phonemes def _phonemes_to_ids(self, phonemes: str) -> List[int]: """Phonemes to ids.""" diff --git a/models/idx_to_token.pkl b/models/idx_to_token.pkl new file mode 100644 index 0000000000000000000000000000000000000000..a5a4baceade8a467e9afde37a8d5049db5a9a760 GIT binary patch literal 447 zcmW;H%QF{n6a{ei>y;jq=;;-O^6Due{($pGm>PsihJJ+^GmMSl7^>4Evapb;O)WB$ zG1I28Ao?HrPn>Z#pS!qs?zz)BAAd&HaZRZnS&SYbnBUnq+>;sW5)yOW_qxkwJP~pj zPla4YuaL*+6Y?4T!U@JRp@1bx;*$^I?uR^=|E_5&*`fMlD XKY#R?Q73dc!*8LR+5fk8lxaQ!Y?G+V literal 0 HcmV?d00001 diff --git a/models/lang_phoneme_dict.pkl b/models/lang_phoneme_dict.pkl new file mode 100644 index 0000000000000000000000000000000000000000..9324bca0e72ea8487f6b21fed6d6dc5fe682a411 GIT 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