From 0367fbf1caf90790a6e7ac0bba8d961268879a39 Mon Sep 17 00:00:00 2001 From: Foxify52 <93794624+Foxify52@users.noreply.github.com> Date: Thu, 15 Feb 2024 20:58:31 -0500 Subject: [PATCH] Format code & implement Gradio WebUI --- README.md | 3 +- RVG.py | 278 +++- lib/fwt/common_layers.py | 144 +- lib/fwt/dsp.py | 140 +- lib/fwt/forward_tacotron.py | 254 +-- lib/fwt/text_utils.py | 235 +-- lib/rvc/transforms.py | 1 + lib/rvc/vc_infer_pipeline.py | 11 +- poetry.lock | 2819 ++++++++++++++++++++++++---------- pyproject.toml | 1 + 10 files changed, 2670 insertions(+), 1216 deletions(-) diff --git a/README.md b/README.md index 232a796..98539c7 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ You can then place them into the model folder with the corresponding names: - `(rvc .pth model name)` -> `rvc_model.pth` - `(rvc .index model name)` -> `rvc_index.index` (optional) -Once you have all of these, you can run the `RVG.py` file with your desired arguments over CLI or you can include this code in your own project and import the `rvg_tts` function from `RVG.py`. +Once you have all of these, you can run the `RVG.py` file with your desired arguments over CLI, run the file without any arguments to launch the Gradio WebUI or you can include this code in your own project and import the `rvg_tts` function from `RVG.py`. ## Current feature set - RVC v1 and v2 model support @@ -29,6 +29,7 @@ Once you have all of these, you can run the `RVG.py` file with your desired argu - [X] Create a proper importable package - [X] Support calling from CLI - [X] Further code condensing + - [X] Gradio WebUI - [ ] Multi-lang support ## Other languages diff --git a/RVG.py b/RVG.py index b127285..2ba9e3f 100644 --- a/RVG.py +++ b/RVG.py @@ -1,62 +1,65 @@ -import torch, os, sys, argparse, winsound, numpy as np +import torch, os, sys, argparse, winsound, numpy as np, gradio as gr from typing import Callable +from fairseq import checkpoint_utils +from scipy.io import wavfile +from multiprocessing import cpu_count from lib.fwt.dsp import DSP from lib.fwt.forward_tacotron import ForwardTacotron from lib.fwt.text_utils import Cleaner, Tokenizer -from multiprocessing import cpu_count from lib.rvc.vc_infer_pipeline import VC -from lib.rvc.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono -from fairseq import checkpoint_utils -from scipy.io import wavfile +from lib.rvc.models import ( + SynthesizerTrnMs256NSFsid, + SynthesizerTrnMs256NSFsid_nono, + SynthesizerTrnMs768NSFsid, + SynthesizerTrnMs768NSFsid_nono, +) + class Synthesizer: - def __init__(self, - tts_path: str, - device='cuda'): + def __init__(self, tts_path: str, device="cuda"): self.device = torch.device(device) tts_checkpoint = torch.load(tts_path, map_location=self.device) - tts_config = tts_checkpoint['config'] + tts_config = tts_checkpoint["config"] tts_model = ForwardTacotron.from_config(tts_config) - tts_model.load_state_dict(tts_checkpoint['model']) + tts_model.load_state_dict(tts_checkpoint["model"]) self.tts_model = tts_model - self.vocoder = torch.hub.load('seungwonpark/melgan', 'melgan', verbose=False) + self.vocoder = torch.hub.load("seungwonpark/melgan", "melgan", verbose=False) self.vocoder.to(device).eval() self.cleaner = Cleaner.from_config(tts_config) self.tokenizer = Tokenizer() self.dsp = DSP.from_config(tts_config) - def __call__(self, - text: str, - voc_model: str, - alpha=1.0, - pitch_function: Callable[[torch.tensor], torch.tensor] = lambda x: x, - energy_function: Callable[[torch.tensor], torch.tensor] = lambda x: x, - ) -> np.array: + def __call__( + self, + text: str, + alpha=1.0, + pitch_function: Callable[[torch.tensor], torch.tensor] = lambda x: x, + energy_function: Callable[[torch.tensor], torch.tensor] = lambda x: x, + ) -> np.array: x = self.cleaner(text) x = self.tokenizer(x) x = torch.tensor(x).unsqueeze(0) - gen = self.tts_model.generate(x, - alpha=alpha, - pitch_function=pitch_function, - energy_function=energy_function) - m = gen['mel_post'].cpu() - if voc_model == 'melgan': - m = m.cuda() - with torch.no_grad(): - wav = self.vocoder.inference(m).cpu().numpy() - else: - print("Specified vocoder isn't supported") - exit() + gen = self.tts_model.generate( + x, + alpha=alpha, + pitch_function=pitch_function, + energy_function=energy_function, + ) + m = gen["mel_post"].cpu() + m = m.cuda() + with torch.no_grad(): + wav = self.vocoder.inference(m).cpu().numpy() return wav -def pcm2float(sig, dtype='float32'): + +def pcm2float(sig, dtype="float32"): sig = np.asarray(sig) - if sig.dtype.kind not in 'iu': + if sig.dtype.kind not in "iu": raise TypeError("'sig' must be an array of integers") dtype = np.dtype(dtype) - if dtype.kind != 'f': + if dtype.kind != "f": raise TypeError("'dtype' must be a floating point type") i = np.iinfo(sig.dtype) @@ -64,8 +67,9 @@ def pcm2float(sig, dtype='float32'): offset = i.min + abs_max return (sig.astype(dtype) - offset) / abs_max + class Config: - def __init__(self,device,is_half): + def __init__(self, device, is_half): self.device = device self.is_half = is_half self.n_cpu = 0 @@ -113,9 +117,13 @@ def device_config(self) -> tuple: return x_pad, x_query, x_center, x_max + def load_hubert(): global hubert_model - models, _, _ = checkpoint_utils.load_model_ensemble_and_task([f'{now_dir}\\models\\hubert.pt'],suffix="",) + models, _, _ = checkpoint_utils.load_model_ensemble_and_task( + [f"{now_dir}\\models\\hubert.pt"], + suffix="", + ) hubert_model = models[0] hubert_model = hubert_model.to(config.device) if config.is_half: @@ -124,20 +132,21 @@ def load_hubert(): hubert_model = hubert_model.float() hubert_model.eval() + def vc_single(sid, audio, f0_up_key, f0_file, file_index, index_rate): - global tgt_sr,net_g,vc,hubert_model, version + global tgt_sr, net_g, vc, hubert_model, version f0_up_key = int(f0_up_key) times = [0, 0, 0] - if(hubert_model==None): + if hubert_model == None: load_hubert() if_f0 = cpt.get("f0", 1) - audio_opt=vc.pipeline( - model=hubert_model, - net_g=net_g, - sid=sid, - audio=audio, - times=times, - f0_up_key=f0_up_key, + audio_opt = vc.pipeline( + model=hubert_model, + net_g=net_g, + sid=sid, + audio=audio, + times=times, + f0_up_key=f0_up_key, file_index=file_index, index_rate=index_rate, if_f0=if_f0, @@ -146,13 +155,14 @@ def vc_single(sid, audio, f0_up_key, f0_file, file_index, index_rate): rms_mix_rate=0.25, version=version, protect=0.5, - f0_file=f0_file + f0_file=f0_file, ) print(times) return audio_opt + def get_vc(model_path): - global n_spk,tgt_sr,net_g,vc,cpt,device,is_half, version + global n_spk, tgt_sr, net_g, vc, cpt, version cpt = torch.load(model_path, map_location="cpu") tgt_sr = cpt["config"][-1] cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] @@ -176,31 +186,32 @@ def get_vc(model_path): else: net_g = net_g.float() vc = VC(tgt_sr, config) - n_spk = cpt["config"][-3] + n_spk = cpt["config"][-3] + def rvg_tts( - input_text="hello world!", - voice_transform=0, - tts_model=f"{os.getcwd()}\\models\\forward.pt", - rvc_model=f"{os.getcwd()}\\models\\rvc_model.pth", - rvc_index=f"{os.getcwd()}\\models\\rvc_index.index", - device="cuda:0", - is_half=True, - silent_mode=False, - persist=True - ): + input_text="hello world!", + voice_transform=0, + tts_model=f"{os.getcwd()}\\models\\forward.pt", + rvc_model=f"{os.getcwd()}\\models\\rvc_model.pth", + rvc_index=f"{os.getcwd()}\\models\\rvc_index.index", + device="cuda:0", + is_half=True, + silent_mode=True, + persist=True, +): global now_dir, config, hubert_model now_dir = os.getcwd() sys.path.append(now_dir) - config=Config(device,is_half) - hubert_model = None if persist else vars().get('hubert_model', None) + config = Config(device, is_half) + hubert_model = None if persist else vars().get("hubert_model", None) synth_forward = Synthesizer(tts_model) - synth_output = pcm2float(synth_forward(input_text, voc_model='melgan', alpha=1.3), dtype=np.float32) + synth_output = pcm2float(synth_forward(input_text, alpha=1.3), dtype=np.float32) get_vc(rvc_model) - wav_opt=vc_single( - sid=0, + wav_opt = vc_single( + sid=0, audio=synth_output, f0_up_key=voice_transform, f0_file=None, @@ -210,15 +221,136 @@ def rvg_tts( wavfile.write("output.wav", tgt_sr, wav_opt) if silent_mode == False: winsound.PlaySound("output.wav", winsound.SND_FILENAME) + else: + return "output.wav" + + +if __name__ == "__main__": + cli_args = [ + "--input_text", + "--voice_transform", + "--tts_model", + "--rvc_model", + "--rvc_index", + "--device", + "--is_half", + "--silent_mode", + ] + + if any(arg in sys.argv for arg in cli_args): + parser = argparse.ArgumentParser( + description="A retrieval based voice generation text to speech system" + ) + parser.add_argument( + "--input_text", + default="hello world!", + type=str, + help="The input text to be converted to speech", + ) + parser.add_argument( + "--voice_transform", + default=0, + type=int, + help="The voice transposition to be applied (Ranges from -12 to 12)", + ) + parser.add_argument( + "--tts_model", + default=f"{os.getcwd()}\\models\\forward.pt", + type=str, + help="The path to the text-to-speech model", + ) + parser.add_argument( + "--rvc_model", + default=f"{os.getcwd()}\\models\\rvc_model.pth", + type=str, + help="The path to the RVC model", + ) + parser.add_argument( + "--rvc_index", + default=f"{os.getcwd()}\\models\\rvc_index.index", + type=str, + help="The path to the RVC index", + ) + parser.add_argument( + "--device", + default="cuda:0", + type=str, + help="The device to run the models on", + ) + parser.add_argument( + "--is_half", + action="store_false", + help="Whether to use half precision for the models", + ) + parser.add_argument( + "--silent_mode", + action="store_false", + help="Whether to suppress the output sound", + ) + args = parser.parse_args() + + rvg_tts(**vars(args)) + + else: + modelDir = f"{os.getcwd()}\\models\\" + + ptList, pthList, indexList = [], [], [] + + for x in os.listdir(modelDir): + if x.endswith(".pt"): + ptList.append(f".\\models\\{x}") + if x.endswith(".pth"): + pthList.append(f".\\models\\{x}") + if x.endswith(".index"): + indexList.append(f".\\models\\{x}") + + device_choices = ["cpu"] + [ + f"cuda:{i}" for i in range(torch.cuda.device_count()) + ] + + interface = gr.Interface( + fn=rvg_tts, + inputs=[ + gr.Textbox( + value="hello world!", + label="Input text", + info="Text to be converted to speech", + lines=3, + ), + gr.Slider( + minimum=-12, + maximum=12, + value=0, + step=1, + label="Voice transform", + info="The voice transposition to be applied", + ), + gr.Dropdown( + choices=ptList, + label="Text-to-speech model", + info="Forward tacotron model", + ), + gr.Dropdown( + choices=pthList, label="RVC voice model", info="RVC voice model" + ), + gr.Dropdown( + choices=indexList, + label="RVC index model", + info="RVC index model (optional)", + ), + gr.Dropdown( + choices=device_choices, + label="Device", + info="Device to run the model on", + ), + gr.Checkbox( + value=True, + label="Use half precision", + info="Whether to use half precision for the models", + ), + ], + outputs=gr.Audio(label="Output audio"), + allow_flagging=False, + ) -parser = argparse.ArgumentParser(description = "A retrieval based voice generation text to speech system") -parser.add_argument("--input_text", default="hello world!", type=str, help="The input text to be converted to speech") -parser.add_argument("--voice_transform", default=0, type=int, help="The voice transposition to be applied (Ranges from -12 to 12)") -parser.add_argument("--tts_model", default=f"{os.getcwd()}\\models\\forward.pt", type=str, help="The path to the text-to-speech model") -parser.add_argument("--rvc_model", default=f"{os.getcwd()}\\models\\rvc_model.pth", type=str, help="The path to the RVC model") -parser.add_argument("--rvc_index", default=f"{os.getcwd()}\\models\\rvc_index.index", type=str, help="The path to the RVC index") -parser.add_argument("--device", default="cuda:0", type=str, help="The device to run the models on") -parser.add_argument("--is_half", action="store_false", help="Whether to use half precision for the models") -parser.add_argument("--silent_mode", action="store_false", help="Whether to suppress the output sound") -args = parser.parse_args() -rvg_tts(**vars(args)) \ No newline at end of file + interface.launch() diff --git a/lib/fwt/common_layers.py b/lib/fwt/common_layers.py index ac56b3f..0d32c02 100644 --- a/lib/fwt/common_layers.py +++ b/lib/fwt/common_layers.py @@ -15,12 +15,12 @@ def __init__(self): super().__init__() def forward(self, x: torch.Tensor, dur: torch.Tensor) -> torch.Tensor: - dur[dur < 0] = 0. + dur[dur < 0] = 0.0 x_expanded = [] for i in range(x.size(0)): x_exp = torch.repeat_interleave(x[i], (dur[i] + 0.5).long(), dim=0) x_expanded.append(x_exp) - x_expanded = pad_sequence(x_expanded, padding_value=0., batch_first=True) + x_expanded = pad_sequence(x_expanded, padding_value=0.0, batch_first=True) return x_expanded @@ -30,24 +30,25 @@ def __init__(self, size: int) -> None: super().__init__() self.W1 = nn.Linear(size, size) self.W2 = nn.Linear(size, size) - self.W1.bias.data.fill_(0.) + self.W1.bias.data.fill_(0.0) def forward(self, x: torch.Tensor) -> torch.Tensor: x1 = self.W1(x) x2 = self.W2(x) g = torch.sigmoid(x2) - y = g * F.relu(x1) + (1. - g) * x + y = g * F.relu(x1) + (1.0 - g) * x return y class BatchNormConv(nn.Module): - def __init__(self, - in_channels: int, - out_channels: int, - kernel: int, relu=True) -> None: + def __init__( + self, in_channels: int, out_channels: int, kernel: int, relu=True + ) -> None: super().__init__() - self.conv = nn.Conv1d(in_channels, out_channels, kernel, stride=1, padding=kernel // 2, bias=False) + self.conv = nn.Conv1d( + in_channels, out_channels, kernel, stride=1, padding=kernel // 2, bias=False + ) self.bnorm = nn.BatchNorm1d(out_channels) self.relu = relu @@ -59,13 +60,15 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: class CBHG(nn.Module): - def __init__(self, - K: int, - in_channels: int, - channels: int, - proj_channels: list, - num_highways: int, - dropout: float = 0.5) -> None: + def __init__( + self, + K: int, + in_channels: int, + channels: int, + proj_channels: list, + num_highways: int, + dropout: float = 0.5, + ) -> None: super().__init__() self.dropout = dropout @@ -77,8 +80,12 @@ def __init__(self, self.maxpool = nn.MaxPool1d(kernel_size=2, stride=1, padding=1) - self.conv_project1 = BatchNormConv(len(self.bank_kernels) * channels, proj_channels[0], 3) - self.conv_project2 = BatchNormConv(proj_channels[0], proj_channels[1], 3, relu=False) + self.conv_project1 = BatchNormConv( + len(self.bank_kernels) * channels, proj_channels[0], 3 + ) + self.conv_project2 = BatchNormConv( + proj_channels[0], proj_channels[1], 3, relu=False + ) self.pre_highway = nn.Linear(proj_channels[-1], channels, bias=False) self.highways = nn.ModuleList() @@ -133,46 +140,59 @@ def __init__(self, d_model: int, dropout=0.1, max_len=5000) -> None: pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) - div_term = torch.exp(torch.arange( - 0, d_model, 2).float() * (-math.log(10000.0) / d_model)) + div_term = torch.exp( + torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model) + ) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0).transpose(0, 1) - self.register_buffer('pe', pe) + self.register_buffer("pe", pe) def forward(self, x: torch.Tensor) -> torch.Tensor: # shape: [T, N] - x = x + self.scale * self.pe[:x.size(0), :] + x = x + self.scale * self.pe[: x.size(0), :] return self.dropout(x) class FFTBlock(nn.Module): - def __init__(self, - d_model: int, - nhead: int, - conv1_kernel: int, - conv2_kernel: int, - d_fft: int, - dropout: float = 0.1): + def __init__( + self, + d_model: int, + nhead: int, + conv1_kernel: int, + conv2_kernel: int, + d_fft: int, + dropout: float = 0.1, + ): super(FFTBlock, self).__init__() self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout) self.dropout = nn.Dropout(dropout) - self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_fft, - kernel_size=conv1_kernel, stride=1, padding=conv1_kernel // 2) - self.conv2 = nn.Conv1d(in_channels=d_fft, out_channels=d_model, - kernel_size=conv2_kernel, stride=1, padding=conv2_kernel // 2) + self.conv1 = nn.Conv1d( + in_channels=d_model, + out_channels=d_fft, + kernel_size=conv1_kernel, + stride=1, + padding=conv1_kernel // 2, + ) + self.conv2 = nn.Conv1d( + in_channels=d_fft, + out_channels=d_model, + kernel_size=conv2_kernel, + stride=1, + padding=conv2_kernel // 2, + ) self.norm1 = LayerNorm(d_model) self.norm2 = LayerNorm(d_model) self.dropout1 = nn.Dropout(dropout) self.dropout2 = nn.Dropout(dropout) self.activation = torch.nn.ReLU() - def forward(self, - src: torch.Tensor, - src_pad_mask: Optional[torch.Tensor] = None) -> torch.Tensor: - src2 = self.self_attn(src, src, src, - attn_mask=None, - key_padding_mask=src_pad_mask)[0] + def forward( + self, src: torch.Tensor, src_pad_mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + src2 = self.self_attn( + src, src, src, attn_mask=None, key_padding_mask=src_pad_mask + )[0] src = src + self.dropout1(src2) src = self.norm1(src) src = src.transpose(0, 1).transpose(1, 2) @@ -187,33 +207,37 @@ def forward(self, class ForwardTransformer(torch.nn.Module): - def __init__(self, - d_model: int, - d_fft: int, - layers: int, - heads: int, - conv1_kernel: int, - conv2_kernel: int, - dropout: float = 0.1, - ) -> None: + def __init__( + self, + d_model: int, + d_fft: int, + layers: int, + heads: int, + conv1_kernel: int, + conv2_kernel: int, + dropout: float = 0.1, + ) -> None: super().__init__() self.d_model = d_model self.pos_encoder = PositionalEncoding(d_model, dropout) - encoder_layer = FFTBlock(d_model=d_model, - nhead=heads, - d_fft=d_fft, - conv1_kernel=conv1_kernel, - conv2_kernel=conv2_kernel, - dropout=dropout) + encoder_layer = FFTBlock( + d_model=d_model, + nhead=heads, + d_fft=d_fft, + conv1_kernel=conv1_kernel, + conv2_kernel=conv2_kernel, + dropout=dropout, + ) encoder_norm = LayerNorm(d_model) - self.layers = nn.ModuleList([copy.deepcopy(encoder_layer) - for _ in range(layers)]) + self.layers = nn.ModuleList( + [copy.deepcopy(encoder_layer) for _ in range(layers)] + ) self.norm = encoder_norm - def forward(self, - x: torch.Tensor, - src_pad_mask: Optional[torch.Tensor] = None) -> torch.Tensor: # shape: [N, T] + def forward( + self, x: torch.Tensor, src_pad_mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: # shape: [N, T] x = x.transpose(0, 1) # shape: [T, N] x = self.pos_encoder(x) for layer in self.layers: @@ -225,7 +249,7 @@ def forward(self, def generate_square_subsequent_mask(sz: int) -> torch.Tensor: mask = torch.triu(torch.ones(sz, sz), 1) - mask = mask.masked_fill(mask == 1, float('-inf')) + mask = mask.masked_fill(mask == 1, float("-inf")) return mask diff --git a/lib/fwt/dsp.py b/lib/fwt/dsp.py index 1a5c225..8a6fb34 100644 --- a/lib/fwt/dsp.py +++ b/lib/fwt/dsp.py @@ -9,36 +9,40 @@ import torchaudio import torchaudio.transforms as transforms + def tensor_to_ndarray(tensor: torch.Tensor) -> np.array: """Convert torch tensor to numpy array""" return tensor.cpu().detach().numpy().squeeze() + def ndarray_to_tensor(array: np.array) -> torch.Tensor: """Convert numpy array to torch tensor""" tensor = torch.from_numpy(array) tensor = torch.unsqueeze(tensor, 0) return tensor + class DSP: - def __init__(self, - num_mels: int, - sample_rate: int, - hop_length: int, - win_length: int, - n_fft: int, - fmin: float, - fmax: float, - peak_norm: bool, - trim_start_end_silence: bool, - trim_silence_top_db: int, - trim_long_silences: bool, - vad_sample_rate: int, - vad_window_length: float, - vad_moving_average_width: float, - vad_max_silence_length: int, - **kwargs, # for backward compatibility - ) -> None: + def __init__( + self, + num_mels: int, + sample_rate: int, + hop_length: int, + win_length: int, + n_fft: int, + fmin: float, + fmax: float, + peak_norm: bool, + trim_start_end_silence: bool, + trim_silence_top_db: int, + trim_long_silences: bool, + vad_sample_rate: int, + vad_window_length: float, + vad_moving_average_width: float, + vad_max_silence_length: int, + **kwargs, # for backward compatibility + ) -> None: self.n_mels = num_mels self.sample_rate = sample_rate @@ -58,15 +62,15 @@ def __init__(self, self.vad_moving_average_width = vad_moving_average_width self.vad_max_silence_length = vad_max_silence_length - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # init transformation self.mel_transform = self._init_mel_transform() @classmethod - def from_config(cls, config: Dict[str, Any]) -> 'DSP': + def from_config(cls, config: Dict[str, Any]) -> "DSP": """Initialize from configuration object""" - return DSP(**config['dsp']) + return DSP(**config["dsp"]) def _init_mel_transform(self): """Initialize mel transformation""" @@ -92,15 +96,15 @@ def load_wav(self, path: Union[str, Path], mono: bool = True) -> torch.Tensor: # merge channels if source is multichannel if mono and metadata.num_channels > 1: - effects.extend([ - ["remix", "-"] # convert to mono - ]) + effects.extend([["remix", "-"]]) # convert to mono # resample if source sample rate is different from desired sample rate if metadata.sample_rate != self.sample_rate: - effects.extend([ - ["rate", f'{self.sample_rate}'], - ]) + effects.extend( + [ + ["rate", f"{self.sample_rate}"], + ] + ) waveform, _ = torchaudio.sox_effects.apply_effects_file(path, effects=effects) return waveform @@ -109,31 +113,48 @@ def save_wav(self, waveform: torch.Tensor, path: Union[str, Path]) -> None: """Save waveform to file""" torchaudio.save(filepath=path, src=waveform, sample_rate=self.sample_rate) - def adjust_volume(self, waveform: torch.Tensor, target_dbfs: int = -30) -> torch.Tensor: + def adjust_volume( + self, waveform: torch.Tensor, target_dbfs: int = -30 + ) -> torch.Tensor: """Adjust volume of the waveform""" - volume_transform = transforms.Vol(gain=target_dbfs, gain_type='db').to(self.device) + volume_transform = transforms.Vol(gain=target_dbfs, gain_type="db").to( + self.device + ) return volume_transform(waveform) - def adjust_volume_batched(self, data: List[torch.Tensor], target_dbfs: int = -30) -> List[torch.Tensor]: + def adjust_volume_batched( + self, data: List[torch.Tensor], target_dbfs: int = -30 + ) -> List[torch.Tensor]: """Adjust volume of the waveforms in the batch""" lengths = [tensor.size(1) for tensor in data] - padded_batch = [torch.nn.functional.pad(x, (0, max(lengths) - x.size(1))) for x in data] + padded_batch = [ + torch.nn.functional.pad(x, (0, max(lengths) - x.size(1))) for x in data + ] stacked_tensor = torch.stack(padded_batch, dim=0) processed_batch = self.adjust_volume(stacked_tensor, target_dbfs=target_dbfs) - result = [processed_waveform[:, :lengths[index]] for index, processed_waveform in enumerate(processed_batch)] + result = [ + processed_waveform[:, : lengths[index]] + for index, processed_waveform in enumerate(processed_batch) + ] return result def waveform_to_mel_batched(self, batch: List[torch.Tensor]) -> List[torch.Tensor]: """Convert waveform to mel spectrogram for the batch of waveforms""" lengths = [tensor.size(1) for tensor in batch] expected_mel_lengths = [x // self.hop_length + 1 for x in lengths] - padded_batch = [torch.nn.functional.pad(x, (0, max(lengths) - x.size(1))) for x in batch] + padded_batch = [ + torch.nn.functional.pad(x, (0, max(lengths) - x.size(1))) for x in batch + ] batch_tensor = torch.stack(padded_batch, dim=0).to(self.device) mels = self.waveform_to_mel(batch_tensor) - list_of_mels = [mel[:, :, :expected_mel_lengths[index]] for index, mel in enumerate(mels)] + list_of_mels = [ + mel[:, :, : expected_mel_lengths[index]] for index, mel in enumerate(mels) + ] return list_of_mels - def waveform_to_mel(self, waveform: torch.Tensor, normalized: bool = True) -> torch.Tensor: + def waveform_to_mel( + self, waveform: torch.Tensor, normalized: bool = True + ) -> torch.Tensor: """Convert waveform to mel spectrogram""" mel_spec = self.mel_transform(waveform) if normalized: @@ -148,18 +169,17 @@ def griffinlim(self, mel: np.array, n_iter: int = 32) -> np.array: sr=self.sample_rate, n_fft=self.n_fft, fmin=self.fmin, - fmax=self.fmax) + fmax=self.fmax, + ) wav = librosa.core.griffinlim( - S, - n_iter=n_iter, - hop_length=self.hop_length, - win_length=self.win_length) + S, n_iter=n_iter, hop_length=self.hop_length, win_length=self.win_length + ) return wav @staticmethod def normalize(mel: torch.Tensor) -> torch.Tensor: """Normalize mel spectrogram""" - mel = torch.clip(mel, min=1.e-5, max=None) + mel = torch.clip(mel, min=1.0e-5, max=None) return torch.log(mel) @staticmethod @@ -170,33 +190,47 @@ def denormalize(mel: np.ndarray) -> np.ndarray: def trim_silence(self, waveform: torch.Tensor) -> torch.Tensor: """Trim silence from the waveform""" waveform = tensor_to_ndarray(waveform) - trimmed_waveform = librosa.effects.trim(waveform, - top_db=self.trim_silence_top_db, - frame_length=self.win_length, - hop_length=self.hop_length) + trimmed_waveform = librosa.effects.trim( + waveform, + top_db=self.trim_silence_top_db, + frame_length=self.win_length, + hop_length=self.hop_length, + ) return ndarray_to_tensor(trimmed_waveform[0]) # borrowed from https://github.com/resemble-ai/Resemblyzer/blob/master/resemblyzer/audio.py def trim_long_silences(self, wav: torch.Tensor) -> torch.Tensor: wav = tensor_to_ndarray(wav) - int16_max = (2 ** 15) - 1 + int16_max = (2**15) - 1 samples_per_window = (self.vad_window_length * self.vad_sample_rate) // 1000 - wav = wav[:len(wav) - (len(wav) % samples_per_window)] - pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16)) + wav = wav[: len(wav) - (len(wav) % samples_per_window)] + pcm_wave = struct.pack( + "%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16) + ) voice_flags = [] vad = webrtcvad.Vad(mode=3) for window_start in range(0, len(wav), samples_per_window): window_end = window_start + samples_per_window - voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2], - sample_rate=self.vad_sample_rate)) + voice_flags.append( + vad.is_speech( + pcm_wave[window_start * 2 : window_end * 2], + sample_rate=self.vad_sample_rate, + ) + ) voice_flags = np.array(voice_flags) + def moving_average(array, width): - array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2))) + array_padded = np.concatenate( + (np.zeros((width - 1) // 2), array, np.zeros(width // 2)) + ) ret = np.cumsum(array_padded, dtype=float) ret[width:] = ret[width:] - ret[:-width] - return ret[width - 1:] / width + return ret[width - 1 :] / width + audio_mask = moving_average(voice_flags, self.vad_moving_average_width) audio_mask = np.round(audio_mask).astype(np.bool) - audio_mask[:] = binary_dilation(audio_mask[:], np.ones(self.vad_max_silence_length + 1)) + audio_mask[:] = binary_dilation( + audio_mask[:], np.ones(self.vad_max_silence_length + 1) + ) audio_mask = np.repeat(audio_mask, samples_per_window) return ndarray_to_tensor(wav[audio_mask]) diff --git a/lib/fwt/forward_tacotron.py b/lib/fwt/forward_tacotron.py index 8fe2db7..27bc27b 100644 --- a/lib/fwt/forward_tacotron.py +++ b/lib/fwt/forward_tacotron.py @@ -16,18 +16,18 @@ class SeriesPredictor(nn.Module): def __init__(self, num_chars, emb_dim=64, conv_dims=256, rnn_dims=64, dropout=0.5): super().__init__() self.embedding = Embedding(num_chars, emb_dim) - self.convs = torch.nn.ModuleList([ - BatchNormConv(emb_dim, conv_dims, 5, relu=True), - BatchNormConv(conv_dims, conv_dims, 5, relu=True), - BatchNormConv(conv_dims, conv_dims, 5, relu=True), - ]) + self.convs = torch.nn.ModuleList( + [ + BatchNormConv(emb_dim, conv_dims, 5, relu=True), + BatchNormConv(conv_dims, conv_dims, 5, relu=True), + BatchNormConv(conv_dims, conv_dims, 5, relu=True), + ] + ) self.rnn = nn.GRU(conv_dims, rnn_dims, batch_first=True, bidirectional=True) self.lin = nn.Linear(2 * rnn_dims, 1) self.dropout = dropout - def forward(self, - x: torch.Tensor, - alpha: float = 1.0) -> torch.Tensor: + def forward(self, x: torch.Tensor, alpha: float = 1.0) -> torch.Tensor: x = self.embedding(x) x = x.transpose(1, 2) for conv in self.convs: @@ -41,70 +41,81 @@ def forward(self, class ForwardTacotron(nn.Module): - def __init__(self, - embed_dims: int, - series_embed_dims: int, - num_chars: int, - durpred_conv_dims: int, - durpred_rnn_dims: int, - durpred_dropout: float, - pitch_conv_dims: int, - pitch_rnn_dims: int, - pitch_dropout: float, - pitch_strength: float, - energy_conv_dims: int, - energy_rnn_dims: int, - energy_dropout: float, - energy_strength: float, - rnn_dims: int, - prenet_dims: int, - prenet_k: int, - postnet_num_highways: int, - prenet_dropout: float, - postnet_dims: int, - postnet_k: int, - prenet_num_highways: int, - postnet_dropout: float, - n_mels: int, - padding_value=-11.5129): + def __init__( + self, + embed_dims: int, + series_embed_dims: int, + num_chars: int, + durpred_conv_dims: int, + durpred_rnn_dims: int, + durpred_dropout: float, + pitch_conv_dims: int, + pitch_rnn_dims: int, + pitch_dropout: float, + pitch_strength: float, + energy_conv_dims: int, + energy_rnn_dims: int, + energy_dropout: float, + energy_strength: float, + rnn_dims: int, + prenet_dims: int, + prenet_k: int, + postnet_num_highways: int, + prenet_dropout: float, + postnet_dims: int, + postnet_k: int, + prenet_num_highways: int, + postnet_dropout: float, + n_mels: int, + padding_value=-11.5129, + ): super().__init__() self.rnn_dims = rnn_dims self.padding_value = padding_value self.embedding = nn.Embedding(num_chars, embed_dims) self.lr = LengthRegulator() - self.dur_pred = SeriesPredictor(num_chars=num_chars, - emb_dim=series_embed_dims, - conv_dims=durpred_conv_dims, - rnn_dims=durpred_rnn_dims, - dropout=durpred_dropout) - self.pitch_pred = SeriesPredictor(num_chars=num_chars, - emb_dim=series_embed_dims, - conv_dims=pitch_conv_dims, - rnn_dims=pitch_rnn_dims, - dropout=pitch_dropout) - self.energy_pred = SeriesPredictor(num_chars=num_chars, - emb_dim=series_embed_dims, - conv_dims=energy_conv_dims, - rnn_dims=energy_rnn_dims, - dropout=energy_dropout) - self.prenet = CBHG(K=prenet_k, - in_channels=embed_dims, - channels=prenet_dims, - proj_channels=[prenet_dims, embed_dims], - num_highways=prenet_num_highways, - dropout=prenet_dropout) - self.lstm = nn.LSTM(2 * prenet_dims, - rnn_dims, - batch_first=True, - bidirectional=True) + self.dur_pred = SeriesPredictor( + num_chars=num_chars, + emb_dim=series_embed_dims, + conv_dims=durpred_conv_dims, + rnn_dims=durpred_rnn_dims, + dropout=durpred_dropout, + ) + self.pitch_pred = SeriesPredictor( + num_chars=num_chars, + emb_dim=series_embed_dims, + conv_dims=pitch_conv_dims, + rnn_dims=pitch_rnn_dims, + dropout=pitch_dropout, + ) + self.energy_pred = SeriesPredictor( + num_chars=num_chars, + emb_dim=series_embed_dims, + conv_dims=energy_conv_dims, + rnn_dims=energy_rnn_dims, + dropout=energy_dropout, + ) + self.prenet = CBHG( + K=prenet_k, + in_channels=embed_dims, + channels=prenet_dims, + proj_channels=[prenet_dims, embed_dims], + num_highways=prenet_num_highways, + dropout=prenet_dropout, + ) + self.lstm = nn.LSTM( + 2 * prenet_dims, rnn_dims, batch_first=True, bidirectional=True + ) self.lin = torch.nn.Linear(2 * rnn_dims, n_mels) - self.register_buffer('step', torch.zeros(1, dtype=torch.long)) - self.postnet = CBHG(K=postnet_k, - in_channels=n_mels, - channels=postnet_dims, - proj_channels=[postnet_dims, n_mels], - num_highways=postnet_num_highways, - dropout=postnet_dropout) + self.register_buffer("step", torch.zeros(1, dtype=torch.long)) + self.postnet = CBHG( + K=postnet_k, + in_channels=n_mels, + channels=postnet_dims, + proj_channels=[postnet_dims, n_mels], + num_highways=postnet_num_highways, + dropout=postnet_dropout, + ) self.post_proj = nn.Linear(2 * postnet_dims, n_mels, bias=False) self.pitch_strength = pitch_strength self.energy_strength = energy_strength @@ -113,15 +124,15 @@ def __init__(self, def __repr__(self): num_params = sum([np.prod(p.size()) for p in self.parameters()]) - return f'ForwardTacotron, num params: {num_params}' + return f"ForwardTacotron, num params: {num_params}" def forward(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: - x = batch['x'] - mel = batch['mel'] - dur = batch['dur'] - mel_lens = batch['mel_len'] - pitch = batch['pitch'].unsqueeze(1) - energy = batch['energy'].unsqueeze(1) + x = batch["x"] + mel = batch["mel"] + dur = batch["dur"] + mel_lens = batch["mel_len"] + pitch = batch["pitch"].unsqueeze(1) + energy = batch["energy"].unsqueeze(1) if self.training: self.step += 1 @@ -144,12 +155,15 @@ def forward(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: x = self.lr(x, dur) - x = pack_padded_sequence(x, lengths=mel_lens.cpu(), enforce_sorted=False, - batch_first=True) + x = pack_padded_sequence( + x, lengths=mel_lens.cpu(), enforce_sorted=False, batch_first=True + ) x, _ = self.lstm(x) - x, _ = pad_packed_sequence(x, padding_value=self.padding_value, batch_first=True) + x, _ = pad_packed_sequence( + x, padding_value=self.padding_value, batch_first=True + ) x = self.lin(x) x = x.transpose(1, 2) @@ -161,52 +175,60 @@ def forward(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: x_post = self._pad(x_post, mel.size(2)) x = self._pad(x, mel.size(2)) - return {'mel': x, 'mel_post': x_post, - 'dur': dur_hat, 'pitch': pitch_hat, 'energy': energy_hat} - - def generate(self, - x: torch.Tensor, - alpha=1.0, - pitch_function: Callable[[torch.Tensor], torch.Tensor] = lambda x: x, - energy_function: Callable[[torch.Tensor], torch.Tensor] = lambda x: x) -> Dict[str, torch.Tensor]: + return { + "mel": x, + "mel_post": x_post, + "dur": dur_hat, + "pitch": pitch_hat, + "energy": energy_hat, + } + + def generate( + self, + x: torch.Tensor, + alpha=1.0, + pitch_function: Callable[[torch.Tensor], torch.Tensor] = lambda x: x, + energy_function: Callable[[torch.Tensor], torch.Tensor] = lambda x: x, + ) -> Dict[str, torch.Tensor]: self.eval() with torch.no_grad(): dur_hat = self.dur_pred(x, alpha=alpha) dur_hat = dur_hat.squeeze(2) if torch.sum(dur_hat.long()) <= 0: - torch.fill_(dur_hat, value=2.) + torch.fill_(dur_hat, value=2.0) pitch_hat = self.pitch_pred(x).transpose(1, 2) pitch_hat = pitch_function(pitch_hat) energy_hat = self.energy_pred(x).transpose(1, 2) energy_hat = energy_function(energy_hat) - return self._generate_mel(x=x, dur_hat=dur_hat, - pitch_hat=pitch_hat, - energy_hat=energy_hat) + return self._generate_mel( + x=x, dur_hat=dur_hat, pitch_hat=pitch_hat, energy_hat=energy_hat + ) @torch.jit.export - def generate_jit(self, - x: torch.Tensor, - alpha: float = 1.0, - beta: float = 1.0) -> Dict[str, torch.Tensor]: + def generate_jit( + self, x: torch.Tensor, alpha: float = 1.0, beta: float = 1.0 + ) -> Dict[str, torch.Tensor]: with torch.no_grad(): dur_hat = self.dur_pred(x, alpha=alpha) dur_hat = dur_hat.squeeze(2) if torch.sum(dur_hat.long()) <= 0: - torch.fill_(dur_hat, value=2.) + torch.fill_(dur_hat, value=2.0) pitch_hat = self.pitch_pred(x).transpose(1, 2) * beta energy_hat = self.energy_pred(x).transpose(1, 2) - return self._generate_mel(x=x, dur_hat=dur_hat, - pitch_hat=pitch_hat, - energy_hat=energy_hat) + return self._generate_mel( + x=x, dur_hat=dur_hat, pitch_hat=pitch_hat, energy_hat=energy_hat + ) def get_step(self) -> int: return self.step.data.item() - def _generate_mel(self, - x: torch.Tensor, - dur_hat: torch.Tensor, - pitch_hat: torch.Tensor, - energy_hat: torch.Tensor) -> Dict[str, torch.Tensor]: + def _generate_mel( + self, + x: torch.Tensor, + dur_hat: torch.Tensor, + pitch_hat: torch.Tensor, + energy_hat: torch.Tensor, + ) -> Dict[str, torch.Tensor]: x = self.embedding(x) x = x.transpose(1, 2) x = self.prenet(x) @@ -230,25 +252,29 @@ def _generate_mel(self, x_post = self.post_proj(x_post) x_post = x_post.transpose(1, 2) - return {'mel': x, 'mel_post': x_post, 'dur': dur_hat, - 'pitch': pitch_hat, 'energy': energy_hat} + return { + "mel": x, + "mel_post": x_post, + "dur": dur_hat, + "pitch": pitch_hat, + "energy": energy_hat, + } def _pad(self, x: torch.Tensor, max_len: int) -> torch.Tensor: x = x[:, :, :max_len] - x = F.pad(x, [0, max_len - x.size(2), 0, 0], 'constant', self.padding_value) + x = F.pad(x, [0, max_len - x.size(2), 0, 0], "constant", self.padding_value) return x - @classmethod - def from_config(cls, config: Dict[str, Any]) -> 'ForwardTacotron': - model_config = config['forward_tacotron']['model'] - model_config['num_chars'] = len(phonemes) - model_config['n_mels'] = config['dsp']['num_mels'] + def from_config(cls, config: Dict[str, Any]) -> "ForwardTacotron": + model_config = config["forward_tacotron"]["model"] + model_config["num_chars"] = len(phonemes) + model_config["n_mels"] = config["dsp"]["num_mels"] return ForwardTacotron(**model_config) @classmethod - def from_checkpoint(cls, path: Union[Path, str]) -> 'ForwardTacotron': - checkpoint = torch.load(path, map_location=torch.device('cpu')) - model = ForwardTacotron.from_config(checkpoint['config']) - model.load_state_dict(checkpoint['model']) - return model \ No newline at end of file + def from_checkpoint(cls, path: Union[Path, str]) -> "ForwardTacotron": + checkpoint = torch.load(path, map_location=torch.device("cpu")) + model = ForwardTacotron.from_config(checkpoint["config"]) + model.load_state_dict(checkpoint["model"]) + return model diff --git a/lib/fwt/text_utils.py b/lib/fwt/text_utils.py index 27f6e6b..8b89f50 100644 --- a/lib/fwt/text_utils.py +++ b/lib/fwt/text_utils.py @@ -6,161 +6,192 @@ from phonemizer.backend.espeak.wrapper import EspeakWrapper from unidecode import unidecode -_ESPEAK_LIBRARY = 'C:\Program Files\eSpeak NG\libespeak-ng.dll' +_ESPEAK_LIBRARY = "C:\Program Files\eSpeak NG\libespeak-ng.dll" EspeakWrapper.set_library(_ESPEAK_LIBRARY) -_pad = '_' -_punctuation = '!\'(),.:;? ' -_special = '-' -_vowels = 'iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ' -_non_pulmonic_consonants = 'ʘɓǀɗǃʄǂɠǁʛ' -_pulmonic_consonants = 'pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ' -_suprasegmentals = 'ˈˌːˑ' -_other_symbols = 'ʍwɥʜʢʡɕʑɺɧ' -_diacrilics = 'ɚ˞ɫ' -_extra_phons = ['g', 'ɝ', '̃', '̍', '̥', '̩', '̯', '͡'] +_pad = "_" +_punctuation = "!'(),.:;? " +_special = "-" +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧ" +_diacrilics = "ɚ˞ɫ" +_extra_phons = ["g", "ɝ", "̃", "̍", "̥", "̩", "̯", "͡"] _inflect = inflect.engine() -_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])') -_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)') -_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)') -_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)') -_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)') -_number_re = re.compile(r'[0-9]+') -phonemes = list( - _pad + _punctuation + _special + _vowels + _non_pulmonic_consonants - + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics) + _extra_phons +_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])") +_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)") +_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)") +_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)") +_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)") +_number_re = re.compile(r"[0-9]+") +phonemes = ( + list( + _pad + + _punctuation + + _special + + _vowels + + _non_pulmonic_consonants + + _pulmonic_consonants + + _suprasegmentals + + _other_symbols + + _diacrilics + ) + + _extra_phons +) phonemes_set = set(phonemes) -silent_phonemes_indices = [i for i, p in enumerate(phonemes) if p in _pad + _punctuation] -_whitespace_re = re.compile(r'\s+') -_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [ - ('mrs', 'misess'), - ('mr', 'mister'), - ('dr', 'doctor'), - ('st', 'saint'), - ('co', 'company'), - ('jr', 'junior'), - ('maj', 'major'), - ('gen', 'general'), - ('drs', 'doctors'), - ('rev', 'reverend'), - ('lt', 'lieutenant'), - ('hon', 'honorable'), - ('sgt', 'sergeant'), - ('capt', 'captain'), - ('esq', 'esquire'), - ('ltd', 'limited'), - ('col', 'colonel'), - ('ft', 'fort'), -]] +silent_phonemes_indices = [ + i for i, p in enumerate(phonemes) if p in _pad + _punctuation +] +_whitespace_re = re.compile(r"\s+") +_abbreviations = [ + (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) + for x in [ + ("mrs", "misess"), + ("mr", "mister"), + ("dr", "doctor"), + ("st", "saint"), + ("co", "company"), + ("jr", "junior"), + ("maj", "major"), + ("gen", "general"), + ("drs", "doctors"), + ("rev", "reverend"), + ("lt", "lieutenant"), + ("hon", "honorable"), + ("sgt", "sergeant"), + ("capt", "captain"), + ("esq", "esquire"), + ("ltd", "limited"), + ("col", "colonel"), + ("ft", "fort"), + ] +] + def _remove_commas(m): - return m.group(1).replace(',', '') + return m.group(1).replace(",", "") + def _expand_decimal_point(m): - return m.group(1).replace('.', ' point ') + return m.group(1).replace(".", " point ") + def _expand_dollars(m): - match = m.group(1) - parts = match.split('.') - if len(parts) > 2: - return match + ' dollars' # Unexpected format - dollars = int(parts[0]) if parts[0] else 0 - cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 - if dollars and cents: - dollar_unit = 'dollar' if dollars == 1 else 'dollars' - cent_unit = 'cent' if cents == 1 else 'cents' - return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit) - elif dollars: - dollar_unit = 'dollar' if dollars == 1 else 'dollars' - return '%s %s' % (dollars, dollar_unit) - elif cents: - cent_unit = 'cent' if cents == 1 else 'cents' - return '%s %s' % (cents, cent_unit) - else: - return 'zero dollars' + match = m.group(1) + parts = match.split(".") + if len(parts) > 2: + return match + " dollars" # Unexpected format + dollars = int(parts[0]) if parts[0] else 0 + cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 + if dollars and cents: + dollar_unit = "dollar" if dollars == 1 else "dollars" + cent_unit = "cent" if cents == 1 else "cents" + return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit) + elif dollars: + dollar_unit = "dollar" if dollars == 1 else "dollars" + return "%s %s" % (dollars, dollar_unit) + elif cents: + cent_unit = "cent" if cents == 1 else "cents" + return "%s %s" % (cents, cent_unit) + else: + return "zero dollars" + def _expand_ordinal(m): - return _inflect.number_to_words(m.group(0)) + return _inflect.number_to_words(m.group(0)) + def _expand_number(m): - num = int(m.group(0)) - if num > 1000 and num < 3000: - if num == 2000: - return 'two thousand' - elif num > 2000 and num < 2010: - return 'two thousand ' + _inflect.number_to_words(num % 100) - elif num % 100 == 0: - return _inflect.number_to_words(num // 100) + ' hundred' + num = int(m.group(0)) + if num > 1000 and num < 3000: + if num == 2000: + return "two thousand" + elif num > 2000 and num < 2010: + return "two thousand " + _inflect.number_to_words(num % 100) + elif num % 100 == 0: + return _inflect.number_to_words(num // 100) + " hundred" + else: + return _inflect.number_to_words( + num, andword="", zero="oh", group=2 + ).replace(", ", " ") else: - return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ') - else: - return _inflect.number_to_words(num, andword='') + return _inflect.number_to_words(num, andword="") + def normalize_numbers(text): - text = re.sub(_comma_number_re, _remove_commas, text) - text = re.sub(_pounds_re, r'\1 pounds', text) - text = re.sub(_dollars_re, _expand_dollars, text) - text = re.sub(_decimal_number_re, _expand_decimal_point, text) - text = re.sub(_ordinal_re, _expand_ordinal, text) - text = re.sub(_number_re, _expand_number, text) - return text + text = re.sub(_comma_number_re, _remove_commas, text) + text = re.sub(_pounds_re, r"\1 pounds", text) + text = re.sub(_dollars_re, _expand_dollars, text) + text = re.sub(_decimal_number_re, _expand_decimal_point, text) + text = re.sub(_ordinal_re, _expand_ordinal, text) + text = re.sub(_number_re, _expand_number, text) + return text + def expand_abbreviations(text): for regex, replacement in _abbreviations: text = re.sub(regex, replacement, text) return text + def collapse_whitespace(text): - return re.sub(_whitespace_re, ' ', text) + return re.sub(_whitespace_re, " ", text) + def no_cleaners(text): return text + def english_cleaners(text): text = unidecode(text) text = normalize_numbers(text) text = expand_abbreviations(text) return text + class Cleaner: - def __init__(self, - cleaner_name: str, - use_phonemes: bool, - lang: str) -> None: - if cleaner_name == 'english_cleaners': + def __init__(self, cleaner_name: str, use_phonemes: bool, lang: str) -> None: + if cleaner_name == "english_cleaners": self.clean_func = english_cleaners - elif cleaner_name == 'no_cleaners': + elif cleaner_name == "no_cleaners": self.clean_func = no_cleaners else: - raise ValueError(f'Cleaner not supported: {cleaner_name}! ' - f'Currently supported: [\'english_cleaners\', \'no_cleaners\']') + raise ValueError( + f"Cleaner not supported: {cleaner_name}! " + f"Currently supported: ['english_cleaners', 'no_cleaners']" + ) self.use_phonemes = use_phonemes self.lang = lang if use_phonemes: - self.backend = EspeakBackend(language=lang, - preserve_punctuation=True, - with_stress=False, - punctuation_marks=';:,.!?¡¿—…"«»“”()', - language_switch='remove-flags') + self.backend = EspeakBackend( + language=lang, + preserve_punctuation=True, + with_stress=False, + punctuation_marks=';:,.!?¡¿—…"«»“”()', + language_switch="remove-flags", + ) def __call__(self, text: str) -> str: text = self.clean_func(text) if self.use_phonemes: text = self.backend.phonemize([text], strip=True)[0] - text = ''.join([p for p in text if p in phonemes_set]) + text = "".join([p for p in text if p in phonemes_set]) text = collapse_whitespace(text) text = text.strip() return text @classmethod - def from_config(cls, config: Dict[str, Any]) -> 'Cleaner': + def from_config(cls, config: Dict[str, Any]) -> "Cleaner": return Cleaner( - cleaner_name=config['preprocessing']['cleaner_name'], - use_phonemes=config['preprocessing']['use_phonemes'], - lang=config['preprocessing']['language'] + cleaner_name=config["preprocessing"]["cleaner_name"], + use_phonemes=config["preprocessing"]["use_phonemes"], + lang=config["preprocessing"]["language"], ) - + + class Tokenizer: def __init__(self) -> None: @@ -172,4 +203,4 @@ def __call__(self, text: str) -> List[int]: def decode(self, sequence: List[int]) -> str: text = [self.id_to_symbol[s] for s in sequence if s in self.id_to_symbol] - return ''.join(text) \ No newline at end of file + return "".join(text) diff --git a/lib/rvc/transforms.py b/lib/rvc/transforms.py index bbf9cdd..223caee 100644 --- a/lib/rvc/transforms.py +++ b/lib/rvc/transforms.py @@ -6,6 +6,7 @@ DEFAULT_MIN_BIN_HEIGHT = 1e-3 DEFAULT_MIN_DERIVATIVE = 1e-3 + def piecewise_rational_quadratic_transform( inputs, unnormalized_widths, diff --git a/lib/rvc/vc_infer_pipeline.py b/lib/rvc/vc_infer_pipeline.py index fd38c4c..5e87dfa 100644 --- a/lib/rvc/vc_infer_pipeline.py +++ b/lib/rvc/vc_infer_pipeline.py @@ -10,10 +10,9 @@ input_audio_path2wav = {} + def change_rms(data1, sr1, data2, sr2, rate): - rms1 = librosa.feature.rms( - y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2 - ) + rms1 = librosa.feature.rms(y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2) rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2) rms1 = torch.from_numpy(rms1) rms1 = F.interpolate( @@ -219,11 +218,7 @@ def pipeline( protect, f0_file=None, ): - if ( - file_index != "" - and os.path.exists(file_index) == True - and index_rate != 0 - ): + if file_index != "" and os.path.exists(file_index) == True and index_rate != 0: try: index = faiss.read_index(file_index) big_npy = index.reconstruct_n(0, index.ntotal) diff --git a/poetry.lock b/poetry.lock index 86df7aa..057c0c3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,40 @@ # This file is automatically @generated by Poetry 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torchcrepe = "0.0.20" unidecode = "1.3.6" webrtcvad = "2.0.10" +gradio = "^4.17.0" [build-system]