203 lines
8.1 KiB
Python
203 lines
8.1 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# * Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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# * Neither the name of the NVIDIA CORPORATION nor the
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# names of its contributors may be used to endorse or promote products
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# derived from this software without specific prior written permission.
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
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# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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import pprint
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import sys
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import time
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import fire
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import torch
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from tqdm import tqdm
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from fastspeech import DEFAULT_DEVICE
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from fastspeech import hparam as hp
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from fastspeech.data_load import PadDataLoader
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from fastspeech.dataset.ljspeech_dataset import LJSpeechDataset
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from fastspeech.model.fastspeech import Fastspeech
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from fastspeech.utils.logging import tprint
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from fastspeech.utils.pytorch import to_cpu_numpy, to_device_async
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from fastspeech.infer import get_inferencer
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from fastspeech.inferencer.waveglow_inferencer import WaveGlowInferencer
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from contextlib import ExitStack
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import numpy as np
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try:
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from apex import amp
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except ImportError:
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ImportError('Required to install apex.')
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pp = pprint.PrettyPrinter(indent=4, width=1000)
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WARMUP_ITERS = 3
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def perf_inference(hparam="infer.yaml",
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with_vocoder=False,
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n_iters=None,
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device=DEFAULT_DEVICE,
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**kwargs):
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"""The script for estimating inference performance.
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By default, this script assumes to load parameters in the default config file, fastspeech/hparams/infer.yaml.
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Besides the flags, you can also set parameters in the config file via the command-line. For examples,
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--dataset_path=DATASET_PATH
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Path to dataset directory.
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--checkpoint_path=CHECKPOINT_PATH
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Path to checkpoint directory. The latest checkpoint will be loaded.
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--batch_size=BATCH_SIZE
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Batch size to use. Defaults to 1.
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Refer to fastspeech/hparams/infer.yaml to see more parameters.
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Args:
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hparam (str, optional): Path to default config file. Defaults to "infer.yaml".
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with_vocoder (bool, optional): Whether or not to estimate with a vocoder. Defaults to False.
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n_iters (int, optional): Number of batches to estimate. Defaults to None (an epoch).
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device (str, optional): Device to use. Defaults to "cuda" if avaiable, or "cpu".
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"""
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hp.set_hparam(hparam, kwargs)
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tprint("Hparams:\n{}".format(pp.pformat(hp)))
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tprint("Device count: {}".format(torch.cuda.device_count()))
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model = Fastspeech(
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max_seq_len=hp.max_seq_len,
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d_model=hp.d_model,
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phoneme_side_n_layer=hp.phoneme_side_n_layer,
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phoneme_side_head=hp.phoneme_side_head,
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phoneme_side_conv1d_filter_size=hp.phoneme_side_conv1d_filter_size,
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phoneme_side_output_size=hp.phoneme_side_output_size,
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mel_side_n_layer=hp.mel_side_n_layer,
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mel_side_head=hp.mel_side_head,
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mel_side_conv1d_filter_size=hp.mel_side_conv1d_filter_size,
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mel_side_output_size=hp.mel_side_output_size,
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duration_predictor_filter_size=hp.duration_predictor_filter_size,
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duration_predictor_kernel_size=hp.duration_predictor_kernel_size,
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fft_conv1d_kernel=hp.fft_conv1d_kernel,
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fft_conv1d_padding=hp.fft_conv1d_padding,
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dropout=hp.dropout,
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n_mels=hp.num_mels,
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fused_layernorm=hp.fused_layernorm
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)
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dataset = LJSpeechDataset(root_path=hp.dataset_path,
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sr=hp.sr,
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n_fft=hp.n_fft,
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win_len=hp.win_len,
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hop_len=hp.hop_len,
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n_mels=hp.num_mels,
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mel_fmin=hp.mel_fmin,
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mel_fmax=hp.mel_fmax,
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exclude_mels=True,
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sort_by_length=True if hp.use_trt and hp.trt_multi_engine else False
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)
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tprint("Dataset size: {}".format(len(dataset)))
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data_loader = PadDataLoader(dataset,
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batch_size=hp.batch_size,
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num_workers=hp.n_workers,
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shuffle=False if hp.use_trt and hp.trt_multi_engine else True,
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drop_last=True,
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)
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fs_inferencer = get_inferencer(model, data_loader, device)
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if with_vocoder:
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if hp.use_trt:
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from fastspeech.trt.waveglow_trt_inferencer import WaveGlowTRTInferencer
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wb_inferencer = WaveGlowTRTInferencer(ckpt_file=hp.waveglow_path, engine_file=hp.waveglow_engine_path, use_fp16=hp.use_fp16)
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else:
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wb_inferencer = WaveGlowInferencer(ckpt_file=hp.waveglow_path, device=device, use_fp16=hp.use_fp16)
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with fs_inferencer, wb_inferencer if with_vocoder else ExitStack():
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tprint("Perf started. Batch size={}.".format(hp.batch_size))
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latencies = []
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throughputs = []
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n_iters = min(n_iters, len(data_loader)) if n_iters else len(data_loader)
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assert(n_iters > WARMUP_ITERS)
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for i in tqdm(range(n_iters)):
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start = time.time()
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outputs = fs_inferencer.infer()
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mels = outputs['mel']
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mel_masks = outputs['mel_mask']
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assert(mels.is_cuda)
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if with_vocoder:
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# remove padding
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max_len = mel_masks.sum(axis=1).max()
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mels = mels[..., :max_len]
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mel_masks = mel_masks[..., :max_len]
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with torch.no_grad():
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wavs = wb_inferencer.infer(mels)
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wavs = to_cpu_numpy(wavs)
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else:
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# include time for DtoH copy
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to_cpu_numpy(mels)
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to_cpu_numpy(mel_masks)
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end = time.time()
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if i > WARMUP_ITERS-1:
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time_elapsed = end - start
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generated_samples = len(mel_masks.nonzero()) * hp.hop_len
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throughput = generated_samples / time_elapsed
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latencies.append(time_elapsed)
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throughputs.append(throughput)
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latencies.sort()
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avg_latency = np.mean(latencies)
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std_latency = np.std(latencies)
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latency_90 = max(latencies[:int(len(latencies)*0.90)]) if n_iters > 1 else 0
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latency_95 = max(latencies[:int(len(latencies)*0.95)]) if n_iters > 1 else 0
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latency_99 = max(latencies[:int(len(latencies)*0.99)]) if n_iters > 1 else 0
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throughput = np.mean(throughputs)
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rtf = throughput / (hp.sr * hp.batch_size)
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tprint("Batch size\tPrecision\tAvg Latency(s)\tStd Latency(s)\tLatency 90%(s)\tLatency 95%(s)\tLatency 99%(s)\tThroughput(samples/s)\tAvg RTF\n\
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{}\t{}\t{:.4f}\t{:.4f}\t{:.4f}\t{:.4f}\t{:.4f}\t{}\t{:.2f}".format(
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hp.batch_size,
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"FP16" if hp.use_fp16 else "FP32",
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avg_latency,
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std_latency,
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latency_90,
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latency_95,
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latency_99,
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int(throughput),
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rtf))
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if __name__ == '__main__':
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fire.Fire(perf_inference)
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