Fhrozen · September 9, 2024 07:35 · Fhrozen · Nov 30, 2021 · sciai-ai · Nov 30, 2021
diff --git a/convert_tts2onnx.py b/convert_tts2onnx.py
 #!/usr/bin/env python3
 """Convert TTS to ONNX
 Using ESPnet.

 Test command:

 python convert_tts2onnx.py --tts-tag espnet/kan-bayashi_ljspeech_vits
 """
 import argparse
 import logging
 import sys
 import numpy as np
 import torch
 import time

 from typing import Dict
 from typing import Optional

 from espnet2.bin.tts_inference import Text2Speech
 from espnet2.utils.types import str_or_none

 import torch.nn.functional as F


 def get_parser():
    parser = argparse.ArgumentParser(
        description="",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    )
    parser.add_argument(
        "--tts-tag",
        required=True,
        type=str,
        help="TTS tag (or Directory) for model located at huggingface/zenodo/local"
    )
    return parser


 ### Add this at espnet2/gan_tts/vits/vits.py
 def inference_onnx(
    self,
    text: torch.Tensor,
    text_lengths: torch.Tensor,
    sids: Optional[torch.Tensor] = None,
    spembs: Optional[torch.Tensor] = None,
    lids: Optional[torch.Tensor] = None,
    durations: Optional[torch.Tensor] = None,
    noise_scale: float = 0.667,
    noise_scale_dur: float = 0.8,
    alpha: float = 1.0,
    max_len: Optional[int] = None,
    use_teacher_forcing: bool = False,
 ) -> Dict[str, torch.Tensor]:
    """Run inference for ONNX.
    """

    if sids is not None:
        sids = sids.view(1)
    if lids is not None:
        lids = lids.view(1)
    if durations is not None:
        durations = durations.view(1, 1, -1)

    # inference
    if use_teacher_forcing:
        raise NotImplementedError
    else:
        wav, _, _ = self.generator.inference(
            text=text,
            text_lengths=text_lengths,
            sids=sids,
            spembs=spembs,
            lids=lids,
            dur=durations,
            noise_scale=noise_scale,
            noise_scale_dur=noise_scale_dur,
            alpha=alpha,
            max_len=max_len,
        )
    return wav.view(-1)


 def test_onnx():
    logging.info('Test ONNX')
    import onnxruntime as ort

    this_text = 'Hello world, how are you doing'
    this_text = preprocessing("<dummy>", dict(text=this_text))['text']
    this_text = this_text[None]
    # this_len = np.array([this_text.shape[1]], dtype=int)
    ort_sess = ort.InferenceSession('tts_model.onnx')

    inname = [input.name for input in ort_sess.get_inputs()]
    outname = [output.name for output in ort_sess.get_outputs()]
    logging.info("inputs name: %s || outputs name: %s", inname, outname)
    outputs = ort_sess.run(None, {'input_text': this_text})
    logging.info(type(outputs))


 if __name__ == "__main__":
    # Logger
    parser = get_parser()
    args = parser.parse_args()
    logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
    logging.basicConfig(filename='onnx.log', encoding='utf-8', level=logging.INFO, format=logfmt)

    # Load Pretrained model and testing wav generation
    logging.info("Preparing pretrained model from: %s", args.tts_tag)

    text2speech = Text2Speech.from_pretrained(
        model_tag=str_or_none(args.tts_tag),
        vocoder_tag=None,
        device="cuda",
        # Only for Tacotron 2 & Transformer
        threshold=0.5,
        # Only for Tacotron 2
        minlenratio=0.0,
        maxlenratio=10.0,
        use_att_constraint=False,
        backward_window=1,
        forward_window=3,
        # Only for FastSpeech & FastSpeech2 & VITS
        speed_control_alpha=1.0,
        # Only for VITS
        noise_scale=0.667,
        noise_scale_dur=0.8,
    )

    text = 'Hello world'
    logging.info("Generating test wav using the sequence: %s", text)
    with torch.no_grad():
        start = time.time()
        wav = text2speech(text)["wav"]
    rtf = (time.time() - start) / (len(wav) / text2speech.fs)
    logging.info(f"RTF = {rtf:5f}")

    # Prepare modules for conversion
    logging.info("Generate ONNX models")
    with torch.no_grad():
        device = text2speech.device
        preprocessing = text2speech.preprocess_fn
        model_tts = text2speech.tts

        # Replace forward with inference to avoid problems at ONNX generation
        model_tts.forward = model_tts.inference_onnx

        # Preprocessing data
        preproc_text = preprocessing("<dummy>", dict(text=text))['text']
        preproc_text = torch.from_numpy(preproc_text).to(device).unsqueeze(0)

        text_lengths = torch.tensor(
            [preproc_text.size(1)],
            dtype=torch.long,
            device=preproc_text.device,
        )
        wav = model_tts(preproc_text, text_lengths)

        logging.info(wav.shape)

        inputs = (preproc_text, text_lengths)
        # Generate TTS Model
        torch.onnx.export(
            model_tts,
            inputs,
            'tts_model.onnx',
            export_params=True,
            opset_version=13,
            do_constant_folding=True,
            verbose=True,
            input_names=['input_text'],
            output_names=['wav'],
            dynamic_axes={
                'input_text': {
                    1: 'length'
                },
                'wav': {
                    0: 'length'
                }
            }
        )

    test_onnx()
    sys.exit(0)
	#!/usr/bin/env python3
	"""Convert TTS to ONNX
	Using ESPnet.

	Test command:

	python convert_tts2onnx.py --tts-tag espnet/kan-bayashi_ljspeech_vits
	"""
	import argparse
	import logging
	import sys
	import numpy as np
	import torch
	import time

	from typing import Dict
	from typing import Optional

	from espnet2.bin.tts_inference import Text2Speech
	from espnet2.utils.types import str_or_none

	import torch.nn.functional as F


	def get_parser():
	parser = argparse.ArgumentParser(
	description="",
	formatter_class=argparse.ArgumentDefaultsHelpFormatter,
	)
	parser.add_argument(
	"--tts-tag",
	required=True,
	type=str,
	help="TTS tag (or Directory) for model located at huggingface/zenodo/local"
	)
	return parser


	### Add this at espnet2/gan_tts/vits/vits.py
	def inference_onnx(
	self,
	text: torch.Tensor,
	text_lengths: torch.Tensor,
	sids: Optional[torch.Tensor] = None,
	spembs: Optional[torch.Tensor] = None,
	lids: Optional[torch.Tensor] = None,
	durations: Optional[torch.Tensor] = None,
	noise_scale: float = 0.667,
	noise_scale_dur: float = 0.8,
	alpha: float = 1.0,
	max_len: Optional[int] = None,
	use_teacher_forcing: bool = False,
	) -> Dict[str, torch.Tensor]:
	"""Run inference for ONNX.
	"""

	if sids is not None:
	sids = sids.view(1)
	if lids is not None:
	lids = lids.view(1)
	if durations is not None:
	durations = durations.view(1, 1, -1)

	# inference
	if use_teacher_forcing:
	raise NotImplementedError
	else:
	wav, _, _ = self.generator.inference(
	text=text,
	text_lengths=text_lengths,
	sids=sids,
	spembs=spembs,
	lids=lids,
	dur=durations,
	noise_scale=noise_scale,
	noise_scale_dur=noise_scale_dur,
	alpha=alpha,
	max_len=max_len,
	)
	return wav.view(-1)


	def test_onnx():
	logging.info('Test ONNX')
	import onnxruntime as ort

	this_text = 'Hello world, how are you doing'
	this_text = preprocessing("<dummy>", dict(text=this_text))['text']
	this_text = this_text[None]
	# this_len = np.array([this_text.shape[1]], dtype=int)
	ort_sess = ort.InferenceSession('tts_model.onnx')

	inname = [input.name for input in ort_sess.get_inputs()]
	outname = [output.name for output in ort_sess.get_outputs()]
	logging.info("inputs name: %s \|\| outputs name: %s", inname, outname)
	outputs = ort_sess.run(None, {'input_text': this_text})
	logging.info(type(outputs))


	if __name__ == "__main__":
	# Logger
	parser = get_parser()
	args = parser.parse_args()
	logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
	logging.basicConfig(filename='onnx.log', encoding='utf-8', level=logging.INFO, format=logfmt)

	# Load Pretrained model and testing wav generation
	logging.info("Preparing pretrained model from: %s", args.tts_tag)

	text2speech = Text2Speech.from_pretrained(
	model_tag=str_or_none(args.tts_tag),
	vocoder_tag=None,
	device="cuda",
	# Only for Tacotron 2 & Transformer
	threshold=0.5,
	# Only for Tacotron 2
	minlenratio=0.0,
	maxlenratio=10.0,
	use_att_constraint=False,
	backward_window=1,
	forward_window=3,
	# Only for FastSpeech & FastSpeech2 & VITS
	speed_control_alpha=1.0,
	# Only for VITS
	noise_scale=0.667,
	noise_scale_dur=0.8,
	)

	text = 'Hello world'
	logging.info("Generating test wav using the sequence: %s", text)
	with torch.no_grad():
	start = time.time()
	wav = text2speech(text)["wav"]
	rtf = (time.time() - start) / (len(wav) / text2speech.fs)
	logging.info(f"RTF = {rtf:5f}")

	# Prepare modules for conversion
	logging.info("Generate ONNX models")
	with torch.no_grad():
	device = text2speech.device
	preprocessing = text2speech.preprocess_fn
	model_tts = text2speech.tts

	# Replace forward with inference to avoid problems at ONNX generation
	model_tts.forward = model_tts.inference_onnx

	# Preprocessing data
	preproc_text = preprocessing("<dummy>", dict(text=text))['text']
	preproc_text = torch.from_numpy(preproc_text).to(device).unsqueeze(0)

	text_lengths = torch.tensor(
	[preproc_text.size(1)],
	dtype=torch.long,
	device=preproc_text.device,
	)
	wav = model_tts(preproc_text, text_lengths)

	logging.info(wav.shape)

	inputs = (preproc_text, text_lengths)
	# Generate TTS Model
	torch.onnx.export(
	model_tts,
	inputs,
	'tts_model.onnx',
	export_params=True,
	opset_version=13,
	do_constant_folding=True,
	verbose=True,
	input_names=['input_text'],
	output_names=['wav'],
	dynamic_axes={
	'input_text': {
	1: 'length'
	},
	'wav': {
	0: 'length'
	}
	}
	)

	test_onnx()
	sys.exit(0)