Merge pull request #133 from iclementine/speedyspeech

Add paddle inference demo with speedyspeech & pwg

examples/speedyspeech/baker/inference.py

@@ -0,0 +1,95 @@
+# Copyright (c) 2021 PaddlePaddle Authors. 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 argparse
+from pathlib import Path
+
+import numpy as np
+from paddle import inference
+import soundfile as sf
+
+from frontend import text_analysis
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Paddle Infernce with speedyspeech & parallel wavegan.")
+    parser.add_argument(
+        "--inference-dir", type=str, help="dir to save inference models")
+    parser.add_argument(
+        "--text",
+        type=str,
+        help="text to synthesize, a 'utt_id sentence' pair per line")
+    parser.add_argument("--output-dir", type=str, help="output dir")
+
+    args = parser.parse_args()
+
+    speedyspeech_config = inference.Config(
+        str(Path(args.inference_dir) / "speedyspeech.pdmodel"),
+        str(Path(args.inference_dir) / "speedyspeech.pdiparams"))
+    speedyspeech_config.enable_use_gpu(100, 0)
+    speedyspeech_config.enable_memory_optim()
+    speedyspeech_predictor = inference.create_predictor(speedyspeech_config)
+
+    pwg_config = inference.Config(
+        str(Path(args.inference_dir) / "pwg.pdmodel"),
+        str(Path(args.inference_dir) / "pwg.pdiparams"))
+    pwg_config.enable_use_gpu(100, 0)
+    pwg_config.enable_memory_optim()
+    pwg_predictor = inference.create_predictor(pwg_config)
+
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    sentences = []
+    with open(args.text, 'rt') as f:
+        for line in f:
+            utt_id, sentence = line.strip().split()
+            sentences.append((utt_id, sentence))
+
+    for utt_id, sentence in sentences:
+        phones, tones = text_analysis(sentence)
+        phones = phones.numpy()
+        tones = tones.numpy()
+
+        input_names = speedyspeech_predictor.get_input_names()
+        phones_handle = speedyspeech_predictor.get_input_handle(input_names[0])
+        tones_handle = speedyspeech_predictor.get_input_handle(input_names[1])
+
+        phones_handle.reshape(phones.shape)
+        phones_handle.copy_from_cpu(phones)
+        tones_handle.reshape(tones.shape)
+        tones_handle.copy_from_cpu(tones)
+
+        speedyspeech_predictor.run()
+        output_names = speedyspeech_predictor.get_output_names()
+        output_handle = speedyspeech_predictor.get_output_handle(output_names[
+            0])
+        output_data = output_handle.copy_to_cpu()
+
+        input_names = pwg_predictor.get_input_names()
+        mel_handle = pwg_predictor.get_input_handle(input_names[0])
+        mel_handle.reshape(output_data.shape)
+        mel_handle.copy_from_cpu(output_data)
+
+        pwg_predictor.run()
+        output_names = pwg_predictor.get_output_names()
+        output_handle = pwg_predictor.get_output_handle(output_names[0])
+        wav = output_data = output_handle.copy_to_cpu()
+
+        sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=24000)
+        print(f"{utt_id} done!")
+
+
+if __name__ == "__main__":
+    main()

examples/speedyspeech/baker/inference.sh

@@ -0,0 +1,5 @@
+python inference.py \
+  --inference-dir=exp/debug/inference \
+  --text=sentences.txt \
+  --output-dir=exp/debug/pd_infer_out
+

parakeet/models/transformer_tts.py

@@ -403,7 +403,7 @@ class TransformerTTS(nn.Layer):
         else:
             self.toned = False
         # position encoding matrix may be extended later
-        self.encoder_pe = pe.sinusoid_positional_encoding(1000, d_encoder)
+        self.encoder_pe = pe.sinusoid_position_encoding(1000, d_encoder)
         self.encoder_pe_scalar = self.create_parameter(
             [1], attr=I.Constant(1.))
         self.encoder = TransformerEncoder(d_encoder, n_heads, d_ffn,
@@ -411,7 +411,7 @@ class TransformerTTS(nn.Layer):
 
         # decoder
         self.decoder_prenet = MLPPreNet(d_mel, d_prenet, d_decoder, dropout)
-        self.decoder_pe = pe.sinusoid_positional_encoding(1000, d_decoder)
+        self.decoder_pe = pe.sinusoid_position_encoding(1000, d_decoder)
         self.decoder_pe_scalar = self.create_parameter(
             [1], attr=I.Constant(1.))
         self.decoder = TransformerDecoder(
@@ -467,7 +467,8 @@ class TransformerTTS(nn.Layer):
             embed += self.tone_embed(tones)
         if embed.shape[1] > self.encoder_pe.shape[0]:
             new_T = max(embed.shape[1], self.encoder_pe.shape[0] * 2)
-            self.encoder_pe = pe.positional_encoding(0, new_T, self.d_encoder)
+            self.encoder_pe = pe.sinusoid_position_encoding(new_T,
+                                                            self.d_encoder)
         pos_enc = self.encoder_pe[:T_enc, :]  # (T, C)
         x = embed.scale(math.sqrt(
             self.d_encoder)) + pos_enc * self.encoder_pe_scalar
@@ -488,8 +489,8 @@ class TransformerTTS(nn.Layer):
         # twice its length if needed
         if x.shape[1] * self.r > self.decoder_pe.shape[0]:
             new_T = max(x.shape[1] * self.r, self.decoder_pe.shape[0] * 2)
-            self.decoder_pe = pe.sinusoid_positional_encoding(new_T,
-                                                              self.d_decoder)
+            self.decoder_pe = pe.sinusoid_position_encoding(new_T,
+                                                            self.d_decoder)
         pos_enc = self.decoder_pe[:T_dec * self.r:self.r, :]
         x = x.scale(math.sqrt(
             self.d_decoder)) + pos_enc * self.decoder_pe_scalar