Merge pull request #677 from rajeevsrao/master

Add Tacotron2 + Waveglow TTS demo

demo/Tacotron2/README.md

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+# Tacotron 2 and WaveGlow Inference with TensorRT
+
+The Tacotron2 and WaveGlow models form a text-to-speech (TTS) system that enables users to synthesize natural sounding speech from raw transcripts without any additional information such as patterns and/or rhythms of speech. This is an implementation of Tacotron2 for PyTorch, tested and maintained by NVIDIA, and provides scripts to perform high-performance inference using NVIDIA TensorRT. More information about the TTS system and its training can be found in the
+[NVIDIA DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/Tacotron2).
+
+NVIDIA TensorRT is a platform for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications. After optimizing the compute-intensive acoustic model with NVIDIA TensorRT, inference throughput increased by up to 1.4x over native PyTorch in mixed  precision.
+
+### Software Versions
+
+Software version configuration tested for the instructions that follow:
+
+|Software|Version|
+|--------|-------|
+|Python|3.6.9|
+|CUDA|11.0.171|
+|Apex|0.1|
+|TensorRT|7.1.3.4|
+|PyTorch|1.5.1|
+
+
+## Quick Start Guide
+
+1. Build and launch the container as described in [TensorRT OSS README](https://github.com/NVIDIA/TensorRT/blob/master/README.md).
+
+    **Note:** After this point, all commands should be run from within the container.
+
+2. Install prerequisite software for TTS sample:
+    ```bash
+    cd $TRT_SOURCE/demo/Tacotron2
+
+    export LD_LIBRARY_PATH=$TRT_SOURCE/build/out:/tensorrt/lib:$LD_LIBRARY_PATH
+    pip3 install /tensorrt/python/tensorrt-7.1*-cp36-none-linux_x86_64.whl
+
+    bash ./scripts/install_prerequisites.sh
+    ```
+3. Download pretrained checkpoints from [NGC](https://ngc.nvidia.com/catalog/models) into the `./checkpoints` directory:
+
+- [Tacotron2 checkpoint](https://ngc.nvidia.com/models/nvidia:tacotron2pyt_fp16)
+- [WaveGlow checkpoint](https://ngc.nvidia.com/models/nvidia:waveglow256pyt_fp16)
+
+    ```bash
+    cd $TRT_SOURCE/demo/Tacotron2
+    ./scripts/download_checkpoints.sh
+    ```
+
+4. Export the models to ONNX intermediate representation (ONNX IR).
+   Export Tacotron 2 to three ONNX parts: Encoder, Decoder, and Postnet:
+
+	```bash
+	mkdir -p output
+    python exports/export_tacotron2_onnx.py --tacotron2 ./checkpoints/tacotron2pyt_fp16_v3/tacotron2_1032590_6000_amp -o output/ --fp16
+	```
+
+    Export WaveGlow to ONNX IR:
+
+	```bash
+    python exports/export_waveglow_onnx.py --waveglow checkpoints/waveglow256pyt_fp16_v2/waveglow_1076430_14000_amp --wn-channels 256 -o output/ --fp16
+	```
+
+	After running the above commands, there should be four new ONNX files in `./output/` directory:
+    `encoder.onnx`, `decoder_iter.onnx`, `postnet.onnx`, and `waveglow.onnx`.
+
+5. Export the ONNX IRs to TensorRT engines with fp16 mode enabled:
+
+	```bash
+	python trt/export_onnx2trt.py --encoder output/encoder.onnx --decoder output/decoder_iter.onnx --postnet output/postnet.onnx --waveglow output/waveglow.onnx -o output/ --fp16
+	```
+
+	After running the command, there should be four new engine files in `./output/` directory:
+    `encoder_fp16.engine`, `decoder_iter_fp16.engine`, `postnet_fp16.engine`, and `waveglow_fp16.engine`.
+
+6. Run TTS inference pipeline with fp16:
+
+	```bash
+	python trt/inference_trt.py -i phrases/phrase.txt --encoder output/encoder_fp16.engine --decoder output/decoder_iter_fp16.engine --postnet output/postnet_fp16.engine --waveglow output/waveglow_fp16.engine -o output/ --fp16
+	```
+
+## Performance
+
+### Benchmarking
+
+The following section shows how to benchmark the TensorRT inference performance for our Tacotron2 + Waveglow TTS.
+
+#### TensorRT inference benchmark
+
+Before running the benchmark script, please download the checkpoints and build the TensorRT engines for the Tacotron2 and Waveglow models as prescribed in the [Quick Start Guide](#quick-start-guide) above.
+
+The inference benchmark is performed on a single GPU by the `inference_benchmark.sh` script, which runs 3 warm-up iterations then runs timed inference for 1000 iterations.
+
+```bash
+bash scripts/inference_benchmark.sh
+```
+
+*Note*: For benchmarking we use WaveGlow with 256 residual channels.
+
+### Results
+
+#### Inference performance: NVIDIA T4 (16GB)
+
+|Framework|Batch size|Input length|Precision|Avg latency (s)|Latency std (s)|Latency confidence interval 90% (s)|Latency confidence interval 95% (s)|Latency confidence interval 99% (s)|Throughput (samples/sec)|Speed-up PyT+TRT/TRT|Avg mels generated (81 mels=1 sec of speech)| Avg audio length (s)| Avg RTF|
+|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+|PyT+TRT|1| 128| FP16| 0.93| 0.15| 1.09| 1.13| 1.49| 169,104| 1.78| 602| 7.35| 7.9|
+|PyT    |1| 128| FP16| 1.58| 0.07| 1.65| 1.70| 1.76|  97,991| 1.00| 605| 6.94| 4.4|
+
+#### Inference performance: NVIDIA V100 (16GB)
+
+|Framework|Batch size|Input length|Precision|Avg latency (s)|Latency std (s)|Latency confidence interval 90% (s)|Latency confidence interval 95% (s)|Latency confidence interval 99% (s)|Throughput (samples/sec)|Speed-up PyT+TRT/TRT|Avg mels generated (81 mels=1 sec of speech)| Avg audio length (s)| Avg RTF|
+|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+|PyT+TRT|1| 128| FP16| 0.63| 0.02| 0.65| 0.66| 0.67| 242,466| 1.78| 599| 7.09| 10.9|
+|PyT    |1| 128| FP16| 1.13| 0.03| 1.17| 1.17| 1.21| 136,160| 1.00| 602| 7.10|  6.3|

demo/Tacotron2/common/audio_processing.py

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+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+import numpy as np
+from scipy.signal import get_window
+import librosa.util as librosa_util
+
+
+def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
+                     n_fft=800, dtype=np.float32, norm=None):
+    """
+    # from librosa 0.6
+    Compute the sum-square envelope of a window function at a given hop length.
+
+    This is used to estimate modulation effects induced by windowing
+    observations in short-time fourier transforms.
+
+    Parameters
+    ----------
+    window : string, tuple, number, callable, or list-like
+        Window specification, as in `get_window`
+
+    n_frames : int > 0
+        The number of analysis frames
+
+    hop_length : int > 0
+        The number of samples to advance between frames
+
+    win_length : [optional]
+        The length of the window function.  By default, this matches `n_fft`.
+
+    n_fft : int > 0
+        The length of each analysis frame.
+
+    dtype : np.dtype
+        The data type of the output
+
+    Returns
+    -------
+    wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
+        The sum-squared envelope of the window function
+    """
+    if win_length is None:
+        win_length = n_fft
+
+    n = n_fft + hop_length * (n_frames - 1)
+    x = np.zeros(n, dtype=dtype)
+
+    # Compute the squared window at the desired length
+    win_sq = get_window(window, win_length, fftbins=True)
+    win_sq = librosa_util.normalize(win_sq, norm=norm)**2
+    win_sq = librosa_util.pad_center(win_sq, n_fft)
+
+    # Fill the envelope
+    for i in range(n_frames):
+        sample = i * hop_length
+        x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))]
+    return x
+
+
+def griffin_lim(magnitudes, stft_fn, n_iters=30):
+    """
+    PARAMS
+    ------
+    magnitudes: spectrogram magnitudes
+    stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods
+    """
+
+    angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size())))
+    angles = angles.astype(np.float32)
+    angles = torch.autograd.Variable(torch.from_numpy(angles))
+    signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
+
+    for i in range(n_iters):
+        _, angles = stft_fn.transform(signal)
+        signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
+    return signal
+
+
+def dynamic_range_compression(x, C=1, clip_val=1e-5):
+    """
+    PARAMS
+    ------
+    C: compression factor
+    """
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression(x, C=1):
+    """
+    PARAMS
+    ------
+    C: compression factor used to compress
+    """
+    return torch.exp(x) / C

demo/Tacotron2/common/layers.py

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+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+from librosa.filters import mel as librosa_mel_fn
+from common.audio_processing import dynamic_range_compression, dynamic_range_decompression
+from common.stft import STFT
+
+
+class LinearNorm(torch.nn.Module):
+    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
+        super(LinearNorm, self).__init__()
+        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
+
+        torch.nn.init.xavier_uniform_(
+            self.linear_layer.weight,
+            gain=torch.nn.init.calculate_gain(w_init_gain))
+
+    def forward(self, x):
+        return self.linear_layer(x)
+
+
+class ConvNorm(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
+                 padding=None, dilation=1, bias=True, w_init_gain='linear'):
+        super(ConvNorm, self).__init__()
+        if padding is None:
+            assert(kernel_size % 2 == 1)
+            padding = int(dilation * (kernel_size - 1) / 2)
+
+        self.conv = torch.nn.Conv1d(in_channels, out_channels,
+                                    kernel_size=kernel_size, stride=stride,
+                                    padding=padding, dilation=dilation,
+                                    bias=bias)
+
+        torch.nn.init.xavier_uniform_(
+            self.conv.weight,
+            gain=torch.nn.init.calculate_gain(w_init_gain))
+
+    def forward(self, signal):
+        return self.conv(signal)
+
+
+class TacotronSTFT(torch.nn.Module):
+    def __init__(self, filter_length=1024, hop_length=256, win_length=1024,
+                 n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0,
+                 mel_fmax=8000.0):
+        super(TacotronSTFT, self).__init__()
+        self.n_mel_channels = n_mel_channels
+        self.sampling_rate = sampling_rate
+        self.stft_fn = STFT(filter_length, hop_length, win_length)
+        mel_basis = librosa_mel_fn(
+            sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax)
+        mel_basis = torch.from_numpy(mel_basis).float()
+        self.register_buffer('mel_basis', mel_basis)
+
+    def spectral_normalize(self, magnitudes):
+        output = dynamic_range_compression(magnitudes)
+        return output
+
+    def spectral_de_normalize(self, magnitudes):
+        output = dynamic_range_decompression(magnitudes)
+        return output
+
+    def mel_spectrogram(self, y):
+        """Computes mel-spectrograms from a batch of waves
+        PARAMS
+        ------
+        y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
+
+        RETURNS
+        -------
+        mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
+        """
+        assert(torch.min(y.data) >= -1)
+        assert(torch.max(y.data) <= 1)
+
+        magnitudes, phases = self.stft_fn.transform(y)
+        magnitudes = magnitudes.data
+        mel_output = torch.matmul(self.mel_basis, magnitudes)
+        mel_output = self.spectral_normalize(mel_output)
+        return mel_output