transformer

examples/synthesize_e2e1.py

@@ -0,0 +1,170 @@
+# 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
+import logging
+from pathlib import Path
+
+import numpy as np
+import paddle
+import soundfile as sf
+import yaml
+from yacs.config import CfgNode
+
+from paddlespeech.t2s.frontend import English,Chinese
+from paddlespeech.t2s.models.transformer_tts import TransformerTTS
+from paddlespeech.t2s.models.transformer_tts import TransformerTTSInference
+from paddlespeech.t2s.models.waveflow import ConditionalWaveFlow
+from paddlespeech.t2s.modules.normalizer import ZScore
+from paddlespeech.t2s.utils import layer_tools
+from paddlespeech.t2s.exps.syn_utils import get_voc_inference2
+from paddlespeech.t2s.exps.syn_utils import get_am_inference
+
+def evaluate(args, acoustic_model_config, vocoder_config):
+    # dataloader has been too verbose
+    logging.getLogger("DataLoader").disabled = True
+
+    # construct dataset for evaluation
+    sentences = []
+    with open(args.text, 'rt') as f:
+        for line in f:
+            line_list = line.strip().split()
+            utt_id = line_list[0]
+            sentence = " ".join(line_list[1:])
+            sentences.append((utt_id, sentence))
+
+    with open(args.phones_dict, "r") as f:
+        phn_id = [line.strip().split() for line in f.readlines()]
+
+    vocab_size = len(phn_id)
+
+    phone_id_map = {}
+    for phn, id in phn_id:
+        phone_id_map[phn] = int(id)
+    print("vocab_size:", vocab_size)
+    odim = acoustic_model_config.n_mels
+    model = TransformerTTS(idim=vocab_size, odim=odim, **acoustic_model_config["model"])
+    model.set_state_dict(paddle.load(args.transformer_tts_checkpoint)["main_params"])
+    model.eval()
+
+    vocoder_checkpoint_path = args.waveflow_checkpoint[:-9] if args.waveflow_checkpoint.endswith(".pdparams") else args.waveflow_checkpoint
+    vocoder = ConditionalWaveFlow.from_pretrained(vocoder_config,vocoder_checkpoint_path)
+    layer_tools.recursively_remove_weight_norm(vocoder)
+    vocoder.eval()
+    print("model done!")
+
+    # vocoder = get_voc_inference2(
+    #     voc=args.voc,
+    #     voc_config=vocoder_config,
+    #     voc_ckpt=args.voc_ckpt,
+    #     voc_stat=args.voc_stat)
+
+    frontend = Chinese()
+    print("frontend done!")
+
+    stat = np.load(args.transformer_tts_stat)
+    mu, std = stat
+    mu = paddle.to_tensor(mu)
+    std = paddle.to_tensor(std)
+    transformer_tts_normalizer = ZScore(mu, std)
+    transformer_tts_inference = TransformerTTSInference(transformer_tts_normalizer, model)
+
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    for utt_id, sentence in sentences:
+        phones = frontend.phoneticize(sentence)
+        print(sentence)
+        print(phones)
+        # remove start_symbol and end_symbol
+        phones = phones[1:-1]
+        phones = [phn for phn in phones if not phn.isspace()]
+        phones = [phn if phn in phone_id_map else "," for phn in phones]
+        phone_ids = [phone_id_map[phn] for phn in phones]
+        print('1',phone_ids)
+        with paddle.no_grad():
+            tensor_phone_ids=paddle.to_tensor(phone_ids)
+            mel = transformer_tts_inference(tensor_phone_ids)
+            # mel shape is (T, feats) and waveflow's input shape is (batch, feats, T)
+            mel = mel.unsqueeze(0).transpose([0, 2, 1])
+            # wavflow's output shape is (B, T)
+            wav = vocoder.infer(mel)[0]
+            #wav = vocoder(mel)[0]
+        sf.write(str(output_dir / (utt_id + ".wav")),wav.numpy(),samplerate=acoustic_model_config.fs)
+        #sf.write(str(output_dir / (utt_id + ".wav")), wav.numpy(), samplerate=24000)
+        print(f"{utt_id} done!")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Synthesize with transformer tts & waveflow.")
+    parser.add_argument(
+        "--transformer-tts-config",
+        default="./out_put/default.yaml",
+        type=str,
+        help="transformer tts config file.")
+    parser.add_argument(
+        "--transformer-tts-checkpoint",
+        default="./out_put/checkpoints/snapshot_iter_1113750.pdz",
+        type=str,
+        help="transformer tts checkpoint to load.")
+    parser.add_argument(
+        "--transformer-tts-stat",
+        default="./dump/speech_stats.npy",
+        type=str,
+        help="mean and standard deviation used to normalize spectrogram when training transformer tts."
+    )
+
+
+    parser.add_argument(
+        "--waveflow-config", default="./waveflow_ljspeech_ckpt_0.3/config.yaml", type=str, help="waveflow config file.")
+    # not normalize when training waveflow
+    parser.add_argument(
+        "--waveflow-checkpoint", default="./waveflow_ljspeech_ckpt_0.3/step-2000000.pdparams", type=str,
+        help="waveflow checkpoint to load.")
+
+    parser.add_argument(
+        "--phones-dict", type=str, default="./dump/phone_id_map.txt", help="phone vocabulary file.")
+    parser.add_argument(
+        "--text",
+        default="./sentences.txt",
+        type=str,
+        help="text to synthesize, a 'utt_id sentence' pair per line.")
+    parser.add_argument("--output-dir", default="./output222", type=str, help="output dir.")
+    parser.add_argument("--ngpu", type=int, default=0, help="if ngpu == 0, use cpu.")
+
+    args = parser.parse_args()
+
+    if args.ngpu == 0:
+        paddle.set_device("cpu")
+    elif args.ngpu > 0:
+        paddle.set_device("gpu")
+    else:
+        print("ngpu should >= 0 !")
+
+    with open(args.transformer_tts_config) as f:
+        transformer_tts_config = CfgNode(yaml.safe_load(f))
+    with open(args.waveflow_config) as f:
+        waveflow_config = CfgNode(yaml.safe_load(f))
+
+    print("========Args========")
+    print(yaml.safe_dump(vars(args)))
+    print("========Config========")
+    print(transformer_tts_config)
+    print(waveflow_config)
+
+    evaluate(args, transformer_tts_config, waveflow_config)
+
+
+if __name__ == "__main__":
+    main()