no message

app.py

@@ -12,7 +12,7 @@ import sys
 if not os.path.exists("israwave-main"):
     with zipfile.ZipFile("israwave-clean.zip", "r") as zip_ref:
         zip_ref.extractall(".")
-    subprocess.check_call([sys.executable, "-m", "pip", "install", "./israwave-clean"])
+    # subprocess.check_call([sys.executable, "-m", "pip", "install", "./israwave-clean"])
 
 # Only after install, import israwave
 from israwave import IsrawaveTTS

israwave/.python-version

@@ -0,0 +1 @@
+3.12

israwave/README.md

@@ -0,0 +1,11 @@
+# israwave
+
+Mission to create a Hebrew TTS model as powerful and user-friendly as WaveNet
+
+## Install
+
+```console
+pip install israwave
+```
+
+See https://github.com/thewh1teagle/israwave

israwave/pyproject.toml

@@ -0,0 +1,22 @@
+[project]
+name = "israwave"
+version = "0.1.8"
+description = "Hebrew text to speech on the edge"
+readme = "README.md"
+authors = [
+    { name = "thewh1teagle", email = "[email protected]" }
+]
+requires-python = ">=3.9"
+dependencies = [
+    "onnxruntime>=1.19.2",
+    "numpy>=2.1.1",
+    "soundfile>=0.12.1",
+    "sounddevice>=0.5.0",
+    "nakdimon-ort>=0.1.5",
+    "espeakng-loader==0.1.2",
+    "phonemizer-fork==3.3.1", # TODO: remove once https://github.com/thewh1teagle/espeakng-loader/issues/2 merged and change dependency from git to stable!
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"

israwave/src/israwave/__init__.py

@@ -0,0 +1,64 @@
+from israwave.tensors import FloatArray
+from .model import Model
+import soundfile as sf
+import os
+from pathlib import Path
+
+class WaveForm:
+    def __init__(self, samples: FloatArray, sample_rate: int) -> None:
+        self.samples =  samples
+        self.sample_rate = sample_rate
+    
+    def save(self, path: str):
+        if os.path.exists(path):
+            os.remove(path)
+        sf.write(path, self.samples, self.sample_rate)
+        
+
+class IsraWave:
+    def __init__(self, model_path: str, espeak_data_path: str) -> None:
+        # Check if the speech model exists
+        if not Path(model_path).exists():
+            raise FileNotFoundError(
+                f"Speech model not found at {model_path}\n"
+                "Please download and prepare the model using the following commands:\n"
+                "wget https://github.com/thewh1teagle/israwave/releases/download/v0.1.0/israwave.onnx"
+            )
+
+        # Check if the espeak data folder exists
+        if not Path(espeak_data_path).exists():
+            raise FileNotFoundError(
+                f"Espeak data folder not found at {espeak_data_path}\n"
+                "Please download and extract the espeak data using the following commands:\n"
+                "wget https://github.com/thewh1teagle/israwave/releases/download/v0.1.0/espeak-ng-data.tar.gz\n"
+                "tar xf espeak-ng-data.tar.gz"
+            )
+
+        self.model = Model(model_path, espeak_data_path)
+        self.sample_rate = self.model.sample_rate
+    
+    def create(self, text, rate = 1.0, pitch = 1.0, energy = 1.0):
+        """create speech waveform
+
+        Args:
+            text str: _description_
+            rate (float, optional): Control rate. Defaults to 1.0.
+            pitch (float, optional): Control pitch. Defaults to 1.0.
+            energy (float, optional): Control energy. Defaults to 1.0.
+
+        Returns:
+            _type_: _description_
+        """
+        inputs = self.model.prepare_input(
+            text,
+            d_factor=rate,
+            p_factor=pitch,
+            e_factor=energy,
+            lang='he'
+        )
+        outputs = self.model.synthesise(inputs)
+        wav = outputs.unbatched_wavs()[0]
+        waveform = WaveForm(wav, self.model.sample_rate)
+        return waveform
+        
+        

israwave/src/israwave/config.json

@@ -0,0 +1,166 @@
+{
+    "symbols": [
+        "_",
+        "^",
+        "$",
+        " ",
+        "!",
+        "\"",
+        "#",
+        "'",
+        "(",
+        ")",
+        ",",
+        "-",
+        ".",
+        "0",
+        "1",
+        "2",
+        "3",
+        "4",
+        "5",
+        "6",
+        "7",
+        "8",
+        "9",
+        ":",
+        ";",
+        "?",
+        "X",
+        "a",
+        "b",
+        "c",
+        "d",
+        "e",
+        "f",
+        "g",
+        "h",
+        "i",
+        "j",
+        "k",
+        "l",
+        "m",
+        "n",
+        "o",
+        "p",
+        "q",
+        "r",
+        "s",
+        "t",
+        "u",
+        "v",
+        "w",
+        "x",
+        "y",
+        "z",
+        "æ",
+        "ç",
+        "ð",
+        "ø",
+        "ħ",
+        "ŋ",
+        "œ",
+        "ǀ",
+        "ǁ",
+        "ǂ",
+        "ǃ",
+        "ɐ",
+        "ɑ",
+        "ɒ",
+        "ɓ",
+        "ɔ",
+        "ɕ",
+        "ɖ",
+        "ɗ",
+        "ɘ",
+        "ə",
+        "ɚ",
+        "ɛ",
+        "ɜ",
+        "ɞ",
+        "ɟ",
+        "ɠ",
+        "ɡ",
+        "ɢ",
+        "ɣ",
+        "ɤ",
+        "ɥ",
+        "ɦ",
+        "ɧ",
+        "ɨ",
+        "ɪ",
+        "ɫ",
+        "ɬ",
+        "ɭ",
+        "ɮ",
+        "ɯ",
+        "ɰ",
+        "ɱ",
+        "ɲ",
+        "ɳ",
+        "ɴ",
+        "ɵ",
+        "ɶ",
+        "ɸ",
+        "ɹ",
+        "ɺ",
+        "ɻ",
+        "ɽ",
+        "ɾ",
+        "ʀ",
+        "ʁ",
+        "ʂ",
+        "ʃ",
+        "ʄ",
+        "ʈ",
+        "ʉ",
+        "ʊ",
+        "ʋ",
+        "ʌ",
+        "ʍ",
+        "ʎ",
+        "ʏ",
+        "ʐ",
+        "ʑ",
+        "ʒ",
+        "ʔ",
+        "ʕ",
+        "ʘ",
+        "ʙ",
+        "ʛ",
+        "ʜ",
+        "ʝ",
+        "ʟ",
+        "ʡ",
+        "ʢ",
+        "ʦ",
+        "ʰ",
+        "ʲ",
+        "ˈ",
+        "ˌ",
+        "ː",
+        "ˑ",
+        "˞",
+        "ˤ",
+        "̃",
+        "̊",
+        "̝",
+        "̧",
+        "̩",
+        "̪",
+        "̯",
+        "̺",
+        "̻",
+        "β",
+        "ε",
+        "θ",
+        "χ",
+        "ᵻ",
+        "↑",
+        "↓",
+        "ⱱ"
+    ],
+    "PAD": "_",
+    "BOS": "^",
+    "EOS": "$"
+}

israwave/src/israwave/helpers.py

@@ -0,0 +1,29 @@
+import numpy as np
+import soundfile as sf
+import io
+
+def text_has_niqqud(text: str) -> bool:
+    return any("\u0591" <= char <= "\u05C7" for char in text)
+
+def text_has_ipa(text: str) -> bool:
+    return any("\u0250" <= char <= "\u02AF" for char in text)
+
+def float_to_int16(samples: np.floating) -> np.int16:
+    """
+    Normalize audio_array if it's floating-point
+    """
+    if np.issubdtype(samples.dtype, np.floating):
+        max_val = np.max(np.abs(samples))
+        samples = (samples / max_val) * 32767 # Normalize to 16-bit range
+        samples = samples.astype(np.int16)
+    return samples
+
+def to_ogg(samples: np.array, sample_rate: int):
+    # Normalize audio_array if it's floating-point
+    samples = float_to_int16(samples)
+    # Create in memory buffer of ogg
+    buf = io.BytesIO()
+    buf.name = 'audio.ogg'
+    sf.write(buf, samples, sample_rate, format="ogg")
+    buf.seek(0)
+    return buf

israwave/src/israwave/logging.py

@@ -0,0 +1,14 @@
+"""
+Provide a way to enable logging by setting LOG_LEVEL environment variable
+"""
+import logging
+import os
+
+# Set default logging level to WARNING if LOG_LEVEL is not set
+log_level = os.getenv("LOG_LEVEL", "WARNING").upper()
+logger = logging.getLogger(__package__)
+logger.setLevel(level=getattr(logging, log_level, logging.WARNING))
+# Setup logging to stdout
+logging.basicConfig(format='%(levelname)s [%(filename)s:%(lineno)d] %(message)s')
+    
+log = logging.getLogger(__package__)

israwave/src/israwave/model.py

@@ -0,0 +1,90 @@
+import json
+from time import perf_counter
+import numpy as np
+import onnxruntime
+from israwave.tokenizer import IPATokenizer
+from .tensors import InferenceInputs, InferenceOutputs
+
+class Model:
+
+    def __init__(self, onnx_path: str, espeak_data_path: str, onnx_providers: list[str] = ["CPUExecutionProvider"]):
+        session = onnxruntime.InferenceSession(onnx_path, providers=onnx_providers)
+        meta = session.get_modelmeta()
+        infer_params = json.loads(meta.custom_metadata_map["inference"])
+        self.tokenizer=IPATokenizer(espeak_data_path)
+        self.session=session
+        self.name=infer_params["name"]
+        self.sample_rate=infer_params["sample_rate"]
+        self.inference_args=infer_params["inference_args"]
+        self.speakers=infer_params["speakers"]
+        self.languages=infer_params["languages"]
+
+    def __post_init__(self):
+        self.is_multispeaker = len(self.speakers) > 1
+        self.is_multilanguage = len(self.languages) > 1
+
+    def prepare_input(
+        self,
+        text: str,
+        lang: str | None = None,
+        speaker: str | int | None = None,
+        d_factor: float|None=None,
+        p_factor: float|None=None,
+        e_factor: float|None=None,
+    ) -> InferenceInputs:
+        sid = None
+        lid = None
+        phids, clean_text = self.tokenizer.tokenize(text=text, language=lang)
+        
+        phids = [phids]
+        input_ids = []
+        lengths = []
+        for phid in phids:
+            input_ids.append(phid)
+            lengths.append(len(phid))
+        sids = [sid] * len(input_ids) if sid is not None else None
+        lids = [lid] * len(input_ids) if lid is not None else None
+        return InferenceInputs.from_ids_and_lengths(
+            ids=input_ids,
+            lengths=lengths,
+            clean_text=clean_text,
+            sids=sids,
+            lids=lids,
+            d_factor=d_factor or self.inference_args["d_factor"],
+            p_factor=p_factor or self.inference_args["p_factor"],
+            e_factor=e_factor or self.inference_args["e_factor"],
+        )
+
+    def synthesise(self, inference_inputs: InferenceInputs) -> InferenceOutputs:
+        inference_inputs = inference_inputs.as_numpy()
+        synth_outs = self.synthesise_with_values(
+            x=inference_inputs.x,
+            x_lengths=inference_inputs.x_lengths,
+            sids=inference_inputs.sids,
+            lids=inference_inputs.lids,
+            d_factor=inference_inputs.d_factor,
+            p_factor=inference_inputs.p_factor,
+            e_factor=inference_inputs.e_factor
+        )
+        return InferenceOutputs(
+            wav=synth_outs["wav"],
+            wav_lengths=synth_outs["wav_lengths"],
+            latency=synth_outs["latency"],
+            rtf=synth_outs["rtf"],
+        )
+
+    def synthesise_with_values(self, x, x_lengths, sids, lids, d_factor, p_factor, e_factor):
+        inputs = dict(
+            x=x,
+            x_lengths=x_lengths,
+            scales=np.array([d_factor, p_factor, e_factor], dtype=np.float32),
+        )
+        t0 = perf_counter()
+        wav, wav_lengths, durations = self.session.run(None, inputs)
+        t_infer = perf_counter() - t0
+        t_audio = wav_lengths.sum() / self.sample_rate
+        rtf = t_infer / t_audio
+        latency = t_infer * 1000
+        return dict(wav=wav, wav_lengths=wav_lengths, rtf=rtf, latency=latency)
+
+

israwave/src/israwave/segment.py

@@ -0,0 +1,51 @@
+import re
+from dataclasses import dataclass
+import numpy as np
+from israwave.logging import log
+
+@dataclass
+class Segment:
+    text: str
+    next_pause: float
+    
+    def create_pause(self, sample_rate: int):
+        """
+        Return array of zeros (silence) with size of the next_pause
+        """
+        num_samples = int(self.next_pause * sample_rate)
+        return np.zeros(num_samples, dtype=np.float32)
+
+class SegmentExtractor:
+    def __init__(self, default_pause: float = 0.05, question_pause: float = 0.05, dot_pause: float = 0.05, new_line_pause = 0.05, hyphen_pause = 0.01):
+        self.default_pause = default_pause
+        self.question_pause = question_pause
+        self.period_pause = dot_pause
+        self.new_line_pause = new_line_pause
+        self.hyphen_pause = hyphen_pause
+    
+    def extract_segments(self, text: str):
+        """
+        Break the text into segment items. eg. on '?' or '.' or '!' or '\n'
+        Used later to add pauses when speaking
+        """
+        log.debug(f'extract segments from {text}')
+        sentences = re.split(r'([•.?!:–\n\-])', text)
+        for i in range(0, len(sentences) - 1, 2):
+            sentence = sentences[i].strip()
+            punctuation = sentences[i + 1]
+            if sentence:  # Ensure the sentence is not empty
+                if punctuation == '.':
+                    yield Segment(text=f"{sentence}{punctuation}", next_pause=self.period_pause)
+                elif punctuation == '?':
+                    yield Segment(text=f"{sentence}{punctuation}", next_pause=self.question_pause)
+                elif punctuation == '\n':
+                    yield Segment(text=f"{sentence}{punctuation}.", next_pause=self.new_line_pause)
+                elif punctuation == '-':
+                    yield Segment(text=f"{sentence}{punctuation}", next_pause=self.hyphen_pause)
+                elif punctuation == '•':
+                    yield Segment(text=f"{sentence}{punctuation}.", next_pause=self.new_line_pause)
+                else:
+                    yield Segment(text=f"{sentence}{punctuation}", next_pause=self.default_pause)
+        last_sentence = sentences[-1].strip()
+        if last_sentence:
+            yield Segment(text=f"{last_sentence}.", next_pause=self.default_pause)

israwave/src/israwave/symbols.py

@@ -0,0 +1,45 @@
+from pathlib import Path
+import json
+
+CONFIG_PATH = Path(__file__).parent / 'config.json'
+with open(CONFIG_PATH, encoding='utf-8') as f:
+    CONFIG = json.load(f)
+    SYMBOLS = CONFIG['symbols']
+   
+# Special symbols
+PAD = CONFIG['PAD']
+BOS = CONFIG['BOS']
+EOS = CONFIG['EOS']
+
+# Special symbol ids
+PAD_ID = SYMBOLS.index(PAD)
+BOS_ID = SYMBOLS.index(BOS)
+EOS_ID = SYMBOLS.index(EOS)
+SPACE_ID = SYMBOLS.index(" ")
+
+# Mappings from symbol to numeric ID and vice versa:
+SYMBOL_TO_ID = {s: i for i, s in enumerate(SYMBOLS)}
+ID_TO_SYMBOL = {i: s for i, s in enumerate(SYMBOLS)}  # pylint: disable=unnecessary-comprehension
+
+
+def phonemes_to_ids(text):
+    """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+    Args:
+      text: string to convert to a sequence
+    Returns:
+      List of integers corresponding to the symbols in the text
+    """
+    sequence = []
+    for symbol in text:
+        symbol_id = SYMBOL_TO_ID[symbol]
+        sequence.append(symbol_id)
+    return sequence
+
+
+def ids_to_phonemes(sequence):
+    """Converts a sequence of IDs back to a string"""
+    result = ""
+    for symbol_id in sequence:
+        s = ID_TO_SYMBOL[symbol_id]
+        result += s
+    return result

israwave/src/israwave/tensors.py

@@ -0,0 +1,129 @@
+import dataclasses
+from dataclasses import dataclass
+from typing import TypeAlias
+import numpy as np
+
+FloatArray: TypeAlias = np.ndarray[np.float32]
+IntArray: TypeAlias = np.ndarray[np.int64]
+
+
+@dataclass
+class BaseTensorContainer:
+
+    def as_tuple(self):
+        return dataclasses.astuple(self)
+
+    def as_dict(self):
+        return dataclasses.asdict(self)
+
+    def as_numpy(self):
+        data = self.as_dict()
+        kwargs = {}
+        for name, value in self.as_dict().items():
+            if isinstance(value, np.ndarray):
+                kwargs[name] = np.asarray(value)
+            else:
+                kwargs[name] = value
+        cls = type(self)
+        return cls(**kwargs)
+
+
+@dataclass(kw_only=True)
+class InferenceInputs(BaseTensorContainer):
+    clean_text: str
+    x: IntArray
+    x_lengths: IntArray
+    sids: IntArray|None = None
+    lids: IntArray|None = None
+    d_factor: float = 1.0
+    p_factor: float = 1.0
+    e_factor: float = 1.0
+
+    @classmethod
+    def from_ids_and_lengths(cls, ids: list[int], lengths: list[int], **kwargs) -> "Self":
+        x = numpy_pad_sequences(ids).astype(np.int64)
+        x_lengths = np.array(lengths, dtype=np.int64)
+        instance = cls(x=x, x_lengths=x_lengths, **kwargs)
+        return instance.as_numpy()
+
+
+@dataclass(kw_only=True)
+class InferenceOutputs(BaseTensorContainer):
+    wav: FloatArray
+    wav_lengths: FloatArray
+    latency: int
+    rtf: float
+    durations: FloatArray|None = None
+    pitch: FloatArray|None = None
+    energy: FloatArray|None = None
+    am_rtf: float|None = None
+    v_rtf: float|None = None
+
+    def __iter__(self):
+        return iter(self.unbatched_wavs())
+
+    def unbatched_wavs(self) -> list[FloatArray]:
+        if isinstance(self.wav, np.ndarray):
+            return numpy_unpad_sequences(self.wav, self.wav_lengths)
+        else:
+            raise RuntimeError("Unsupported operation")
+
+
+def numpy_pad_sequences(sequences, maxlen=None, value=0):
+    """Pads a list of sequences to the same length using broadcasting.
+
+    Args:
+      sequences: A list of Python lists with variable lengths.
+      maxlen: The maximum length to pad the sequences to. If not specified,
+        the maximum length of all sequences in the list will be used.
+      value: The value to use for padding (default 0).
+
+    Returns:
+      A numpy array with shape [batch_size, maxlen] where the sequences are padded
+      with the specified value.
+    """
+
+    # Get the maximum length if not specified
+    if maxlen is None:
+        maxlen = max(len(seq) for seq in sequences)
+
+    # Create a numpy array with the specified value and broadcast
+    padded_seqs = np.full((len(sequences), maxlen), value)
+    for i, seq in enumerate(sequences):
+        padded_seqs[i, : len(seq)] = seq
+
+    return padded_seqs
+
+
+def numpy_unpad_sequences(sequences, lengths):
+    """Unpads a list of sequences based on a list of lengths.
+
+    Args:
+      sequences: A numpy array with shape [batch_size, feature_dim, max_len].
+      lengths: A numpy array with shape [batch_size] representing the lengths
+        of each sequence in the batch.
+
+    Returns:
+      A list of unpadded sequences. The i-th element of the list corresponds
+      to the i-th sequence in the batch. Each sequence is a numpy array with
+      variable length.
+    """
+
+    # Check if lengths argument is a list or 1D numpy array
+    if not isinstance(lengths, np.ndarray) or len(lengths.shape) != 1:
+        raise ValueError("lengths must be a 1D numpy array")
+
+    # Check if sequence lengths are within bounds
+    if np.any(lengths < 0) or np.any(lengths > sequences.shape[-1]):
+        raise ValueError("lengths must be between 0 and max_len")
+
+    # Get the batch size
+    batch_size = sequences.shape[0]
+
+    # Extract unpadded sequences
+    unpadded_seqs = []
+    for i in range(batch_size):
+        unpadded_seqs.append(sequences[i, : lengths[i]])
+
+    return unpadded_seqs
+

israwave/src/israwave/tokenizer.py

@@ -0,0 +1,40 @@
+import re
+from .symbols import phonemes_to_ids
+from israwave.logging import log
+from phonemizer.backend.espeak.wrapper import EspeakWrapper
+import phonemizer
+import espeakng_loader
+
+WHITESPACE_RE = re.compile(r"\s+")
+
+class IPATokenizer:
+    def __init__(self, espeak_data_path = None) -> None:
+        EspeakWrapper.set_library(espeakng_loader.get_library_path())
+        EspeakWrapper.set_data_path(espeak_data_path or espeakng_loader.get_data_path())
+    
+    def preprocess_text(self, text, _language):
+        return self.collapse_whitespace(text)
+    
+    def collapse_whitespace(self, text):
+        text = re.sub(WHITESPACE_RE, " ", text)
+        return text
+    
+    def phonemize_text(self, text: str, language: str) -> str:
+        # Preprocess
+        text = self.preprocess_text(text, language)
+        # Phonemize
+        phonemes = phonemizer.phonemize(text, language, preserve_punctuation=True, with_stress=True)        
+        return phonemes, text
+    
+    def tokenize(self, text, language):
+        try:
+            # Accept phonemes directly
+            phoneme_ids, normalized_text = phonemes_to_ids(text), self.preprocess_text(text, 'he')
+        except:
+            # Create phoenems
+            phonemes, normalized_text = self.phonemize_text(text, language)
+            phonemes = [phoneme for sentence_phonemes in phonemes for phoneme in sentence_phonemes]
+            phonemes = list(self.collapse_whitespace("".join(phonemes)))
+            phoneme_ids = phonemes_to_ids(phonemes)
+            log.debug(f"phonemes: {''.join(phonemes)} text: {text}")
+        return phoneme_ids, normalized_text

israwave/uv.lock

@@ -0,0 +1,282 @@
+version = 1
+requires-python = ">=3.12"
+
+[[package]]
+name = "cffi"
+version = "1.17.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pycparser" },
+]
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