mirror of https://github.com/rhasspy/piper
Michael Hansen
2 weeks ago
committed by
GitHub
commit
078bf8a17e
@ -0,0 +1,196 @@
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#!/usr/bin/env python3
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import argparse
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import logging
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import os
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from pathlib import Path
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from typing import Optional
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import torch
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from torch import nn
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from .vits import commons
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from .vits.lightning import VitsModel
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_LOGGER = logging.getLogger("piper_train.export_onnx")
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OPSET_VERSION = 15
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class VitsEncoder(nn.Module):
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def __init__(self, gen):
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super().__init__()
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self.gen = gen
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def forward(self, x, x_lengths, scales, sid=None):
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noise_scale = scales[0]
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length_scale = scales[1]
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noise_scale_w = scales[2]
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gen = self.gen
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x, m_p, logs_p, x_mask = gen.enc_p(x, x_lengths)
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if gen.n_speakers > 1:
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assert sid is not None, "Missing speaker id"
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g = gen.emb_g(sid).unsqueeze(-1) # [b, h, 1]
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else:
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g = None
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if gen.use_sdp:
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logw = gen.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
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else:
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logw = gen.dp(x, x_mask, g=g)
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w = torch.exp(logw) * x_mask * length_scale
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w_ceil = torch.ceil(w)
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y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
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y_mask = torch.unsqueeze(
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commons.sequence_mask(y_lengths, y_lengths.max()), 1
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).type_as(x_mask)
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attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
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attn = commons.generate_path(w_ceil, attn_mask)
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m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
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1, 2
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) # [b, t', t], [b, t, d] -> [b, d, t']
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logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
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1, 2
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) # [b, t', t], [b, t, d] -> [b, d, t']
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z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
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return z_p, y_mask, g
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class VitsDecoder(nn.Module):
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def __init__(self, gen):
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super().__init__()
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self.gen = gen
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def forward(self, z, y_mask, g=None):
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z = self.gen.flow(z, y_mask, g=g, reverse=True)
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output = self.gen.dec((z * y_mask), g=g)
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return output
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def main() -> None:
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"""Main entry point"""
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torch.manual_seed(1234)
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parser = argparse.ArgumentParser()
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parser.add_argument("checkpoint", help="Path to model checkpoint (.ckpt)")
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parser.add_argument("output_dir", help="Path to output directory")
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parser.add_argument(
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"--debug", action="store_true", help="Print DEBUG messages to the console"
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)
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args = parser.parse_args()
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if args.debug:
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logging.basicConfig(level=logging.DEBUG)
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else:
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logging.basicConfig(level=logging.INFO)
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_LOGGER.debug(args)
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# -------------------------------------------------------------------------
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args.checkpoint = Path(args.checkpoint)
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args.output_dir = Path(args.output_dir)
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args.output_dir.mkdir(parents=True, exist_ok=True)
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model = VitsModel.load_from_checkpoint(args.checkpoint, dataset=None)
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model_g = model.model_g
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with torch.no_grad():
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model_g.dec.remove_weight_norm()
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_LOGGER.info("Exporting encoder...")
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decoder_input = export_encoder(args, model_g)
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_LOGGER.info("Exporting decoder...")
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export_decoder(args, model_g, decoder_input)
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_LOGGER.info("Exported model to %s", str(args.output_dir))
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def export_encoder(args, model_g):
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model = VitsEncoder(model_g)
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model.eval()
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num_symbols = model_g.n_vocab
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num_speakers = model_g.n_speakers
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dummy_input_length = 50
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sequences = torch.randint(
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low=0, high=num_symbols, size=(1, dummy_input_length), dtype=torch.long
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)
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sequence_lengths = torch.LongTensor([sequences.size(1)])
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sid: Optional[torch.LongTensor] = None
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if num_speakers > 1:
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sid = torch.LongTensor([0])
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# noise, noise_w, length
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scales = torch.FloatTensor([0.667, 1.0, 0.8])
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dummy_input = (sequences, sequence_lengths, scales, sid)
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output_names = [
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"z",
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"y_mask",
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]
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if model_g.n_speakers > 1:
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output_names.append("g")
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onnx_path = os.fspath(args.output_dir.joinpath("encoder.onnx"))
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# Export
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torch.onnx.export(
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model=model,
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args=dummy_input,
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f=onnx_path,
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verbose=False,
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opset_version=OPSET_VERSION,
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input_names=["input", "input_lengths", "scales", "sid"],
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output_names=output_names,
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dynamic_axes={
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"input": {0: "batch_size", 1: "phonemes"},
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"input_lengths": {0: "batch_size"},
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"output": {0: "batch_size", 2: "time"},
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},
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)
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_LOGGER.info("Exported encoder to %s", onnx_path)
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return model(*dummy_input)
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def export_decoder(args, model_g, decoder_input):
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model = VitsDecoder(model_g)
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model.eval()
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input_names = [
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"z",
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"y_mask",
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]
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if model_g.n_speakers > 1:
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input_names.append("g")
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onnx_path = os.fspath(args.output_dir.joinpath("decoder.onnx"))
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# Export
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torch.onnx.export(
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model=model,
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args=decoder_input,
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f=onnx_path,
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verbose=False,
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opset_version=OPSET_VERSION,
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input_names=input_names,
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output_names=["output"],
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dynamic_axes={
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"z": {0: "batch_size", 2: "time"},
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"y_mask": {0: "batch_size", 2: "time"},
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"output": {0: "batch_size", 1: "time"},
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},
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)
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_LOGGER.info("Exported decoder to %s", onnx_path)
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# -----------------------------------------------------------------------------
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if __name__ == "__main__":
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main()
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#!/usr/bin/env python3
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import argparse
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import json
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import logging
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import math
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import os
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import sys
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import time
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from pathlib import Path
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import numpy as np
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import onnxruntime
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from .vits.utils import audio_float_to_int16
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_LOGGER = logging.getLogger("piper_train.infer_onnx")
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class SpeechStreamer:
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"""
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Stream speech in real time.
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Args:
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encoder_path: path to encoder ONNX model
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decoder_path: path to decoder ONNX model
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sample_rate: output sample rate
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chunk_size: number of mel frames to decode in each steps (time in secs = chunk_size * 256)
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chunk_padding: number of mel frames to be concatinated to the start and end of the current chunk to reduce decoding artifacts
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"""
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def __init__(
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self,
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encoder_path,
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decoder_path,
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sample_rate,
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chunk_size=45,
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chunk_padding=10,
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):
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sess_options = onnxruntime.SessionOptions()
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_LOGGER.debug("Loading encoder model from %s", encoder_path)
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self.encoder = onnxruntime.InferenceSession(
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encoder_path, sess_options=sess_options
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)
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_LOGGER.debug("Loading decoder model from %s", decoder_path)
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self.decoder = onnxruntime.InferenceSession(
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decoder_path, sess_options=sess_options
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)
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self.sample_rate = sample_rate
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self.chunk_size = chunk_size
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self.chunk_padding = chunk_padding
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def encoder_infer(self, enc_input):
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ENC_START = time.perf_counter()
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enc_output = self.encoder.run(None, enc_input)
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ENC_INFER = time.perf_counter() - ENC_START
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_LOGGER.debug(f"Encoder inference {round(ENC_INFER * 1000)}")
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wav_length = enc_output[0].shape[2] * 256
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enc_rtf = round(ENC_INFER / (wav_length / self.sample_rate), 2)
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_LOGGER.debug(f"Encoder RTF {enc_rtf}")
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return enc_output
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def decoder_infer(self, z, y_mask, g=None):
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dec_input = {"z": z, "y_mask": y_mask}
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if g:
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dec_input["g"] = g
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DEC_START = time.perf_counter()
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audio = self.decoder.run(None, dec_input)[0].squeeze()
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DEC_INFER = time.perf_counter() - DEC_START
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_LOGGER.debug(f"Decoder inference {round(DEC_INFER * 1000)}")
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dec_rtf = round(DEC_INFER / (len(audio) / self.sample_rate), 2)
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_LOGGER.debug(f"Decoder RTF {dec_rtf}")
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return audio
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def chunk(self, enc_output):
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z, y_mask, *dec_args = enc_output
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n_frames = z.shape[2]
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if n_frames <= (self.chunk_size + (2 * self.chunk_padding)):
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# Too short to stream
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return self.decoder_infer(z, y_mask, *dec_args)
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split_at = [
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i * self.chunk_size for i in range(1, math.ceil(n_frames / self.chunk_size))
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]
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chunks = list(
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zip(
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np.split(z, split_at, axis=2),
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np.split(y_mask, split_at, axis=2),
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)
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)
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wav_start_pad = wav_end_pad = None
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for idx, (z_chunk, y_mask_chunk) in enumerate(chunks):
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if idx > 0:
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prev_z, prev_y_mask = chunks[idx - 1]
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start_zpad = prev_z[:, :, -self.chunk_padding :]
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start_ypad = prev_y_mask[:, :, -self.chunk_padding :]
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z_chunk = np.concatenate([start_zpad, z_chunk], axis=2)
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y_mask_chunk = np.concatenate([start_ypad, y_mask_chunk], axis=2)
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wav_start_pad = start_zpad.shape[2] * 256
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if (idx + 1) < len(chunks):
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next_z, next_y_mask = chunks[idx + 1]
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end_zpad = next_z[:, :, : self.chunk_padding]
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end_ypad = next_y_mask[:, :, : self.chunk_padding]
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z_chunk = np.concatenate([z_chunk, end_zpad], axis=2)
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y_mask_chunk = np.concatenate([y_mask_chunk, end_ypad], axis=2)
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wav_end_pad = end_zpad.shape[2] * 256
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audio = self.decoder_infer(z_chunk, y_mask_chunk, *dec_args)
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yield audio[wav_start_pad:-wav_end_pad]
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def stream(self, encoder_input):
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start_time = time.perf_counter()
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has_shown_latency = False
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_LOGGER.debug("Starting synthesis")
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enc_output = self.encoder_infer(encoder_input)
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for wav in self.chunk(enc_output):
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if len(wav) == 0:
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continue
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if not has_shown_latency:
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LATENCY = round((time.perf_counter() - start_time) * 1000)
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_LOGGER.debug(f"Latency {LATENCY}")
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has_shown_latency = True
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audio = audio_float_to_int16(wav)
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yield audio.tobytes()
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_LOGGER.debug("Synthesis done!")
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def main():
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"""Main entry point"""
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logging.basicConfig(level=logging.DEBUG)
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parser = argparse.ArgumentParser(prog="piper_train.infer_onnx_streaming")
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parser.add_argument(
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"--encoder", required=True, help="Path to encoder model (.onnx)"
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)
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parser.add_argument(
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"--decoder", required=True, help="Path to decoder model (.onnx)"
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)
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parser.add_argument("--sample-rate", type=int, default=22050)
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parser.add_argument("--noise-scale", type=float, default=0.667)
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parser.add_argument("--noise-scale-w", type=float, default=0.8)
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parser.add_argument("--length-scale", type=float, default=1.0)
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parser.add_argument(
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"--chunk-size",
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type=int,
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default=45,
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help="Number of mel frames to decode at each step"
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)
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parser.add_argument(
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"--chunk-padding",
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type=int,
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default=5,
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help="Number of mel frames to add to the start and end of the current chunk to reduce decoding artifacts"
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)
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args = parser.parse_args()
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streamer = SpeechStreamer(
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encoder_path=os.fspath(args.encoder),
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decoder_path=os.fspath(args.decoder),
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sample_rate=args.sample_rate,
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chunk_size=args.chunk_size,
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chunk_padding=args.chunk_padding,
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)
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output_buffer = sys.stdout.buffer
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for i, line in enumerate(sys.stdin):
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line = line.strip()
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if not line:
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continue
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utt = json.loads(line)
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utt_id = str(i)
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phoneme_ids = utt["phoneme_ids"]
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speaker_id = utt.get("speaker_id")
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text = np.expand_dims(np.array(phoneme_ids, dtype=np.int64), 0)
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text_lengths = np.array([text.shape[1]], dtype=np.int64)
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scales = np.array(
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[args.noise_scale, args.length_scale, args.noise_scale_w],
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dtype=np.float32,
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)
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sid = None
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if speaker_id is not None:
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sid = np.array([speaker_id], dtype=np.int64)
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stream = streamer.stream(
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{
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"input": text,
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"input_lengths": text_lengths,
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"scales": scales,
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"sid": sid,
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}
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)
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for wav_chunk in stream:
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output_buffer.write(wav_chunk)
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output_buffer.flush()
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def denoise(
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audio: np.ndarray, bias_spec: np.ndarray, denoiser_strength: float
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) -> np.ndarray:
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audio_spec, audio_angles = transform(audio)
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a = bias_spec.shape[-1]
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b = audio_spec.shape[-1]
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repeats = max(1, math.ceil(b / a))
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bias_spec_repeat = np.repeat(bias_spec, repeats, axis=-1)[..., :b]
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audio_spec_denoised = audio_spec - (bias_spec_repeat * denoiser_strength)
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audio_spec_denoised = np.clip(audio_spec_denoised, a_min=0.0, a_max=None)
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audio_denoised = inverse(audio_spec_denoised, audio_angles)
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return audio_denoised
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def stft(x, fft_size, hopsamp):
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"""Compute and return the STFT of the supplied time domain signal x.
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Args:
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x (1-dim Numpy array): A time domain signal.
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fft_size (int): FFT size. Should be a power of 2, otherwise DFT will be used.
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hopsamp (int):
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Returns:
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The STFT. The rows are the time slices and columns are the frequency bins.
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"""
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window = np.hanning(fft_size)
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fft_size = int(fft_size)
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hopsamp = int(hopsamp)
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return np.array(
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[
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np.fft.rfft(window * x[i : i + fft_size])
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for i in range(0, len(x) - fft_size, hopsamp)
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]
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)
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def istft(X, fft_size, hopsamp):
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"""Invert a STFT into a time domain signal.
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Args:
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X (2-dim Numpy array): Input spectrogram. The rows are the time slices and columns are the frequency bins.
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fft_size (int):
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hopsamp (int): The hop size, in samples.
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Returns:
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The inverse STFT.
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"""
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fft_size = int(fft_size)
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hopsamp = int(hopsamp)
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window = np.hanning(fft_size)
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time_slices = X.shape[0]
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len_samples = int(time_slices * hopsamp + fft_size)
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x = np.zeros(len_samples)
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for n, i in enumerate(range(0, len(x) - fft_size, hopsamp)):
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x[i : i + fft_size] += window * np.real(np.fft.irfft(X[n]))
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return x
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def inverse(magnitude, phase):
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recombine_magnitude_phase = np.concatenate(
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[magnitude * np.cos(phase), magnitude * np.sin(phase)], axis=1
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)
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x_org = recombine_magnitude_phase
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n_b, n_f, n_t = x_org.shape # pylint: disable=unpacking-non-sequence
|
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x = np.empty([n_b, n_f // 2, n_t], dtype=np.complex64)
|
||||
x.real = x_org[:, : n_f // 2]
|
||||
x.imag = x_org[:, n_f // 2 :]
|
||||
inverse_transform = []
|
||||
for y in x:
|
||||
y_ = istft(y.T, fft_size=1024, hopsamp=256)
|
||||
inverse_transform.append(y_[None, :])
|
||||
|
||||
inverse_transform = np.concatenate(inverse_transform, 0)
|
||||
|
||||
return inverse_transform
|
||||
|
||||
|
||||
def transform(input_data):
|
||||
x = input_data
|
||||
real_part = []
|
||||
imag_part = []
|
||||
for y in x:
|
||||
y_ = stft(y, fft_size=1024, hopsamp=256).T
|
||||
real_part.append(y_.real[None, :, :]) # pylint: disable=unsubscriptable-object
|
||||
imag_part.append(y_.imag[None, :, :]) # pylint: disable=unsubscriptable-object
|
||||
real_part = np.concatenate(real_part, 0)
|
||||
imag_part = np.concatenate(imag_part, 0)
|
||||
|
||||
magnitude = np.sqrt(real_part**2 + imag_part**2)
|
||||
phase = np.arctan2(imag_part.data, real_part.data)
|
||||
|
||||
return magnitude, phase
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in New Issue