modified data preprocessing and synthesis of transformer_tts and fastspeech

examples/fastspeech/README.md

@@ -87,7 +87,7 @@ python train.py \
 --use_gpu=1 \
 --data=${DATAPATH} \
 --alignments_path=${ALIGNMENTS_PATH} \
---output='./experiment' \
+--output=${OUTPUTPATH} \
 --config='configs/ljspeech.yaml' \
 ```
 
@@ -105,7 +105,7 @@ python -m paddle.distributed.launch --selected_gpus=0,1,2,3 --log_dir ./mylog tr
 --use_gpu=1 \
 --data=${DATAPATH} \
 --alignments_path=${ALIGNMENTS_PATH} \
---output='./experiment' \
+--output=${OUTPUTPATH} \
 --config='configs/ljspeech.yaml' \
 ```
 
@@ -123,14 +123,13 @@ After training the FastSpeech, audio can be synthesized by running ``synthesis.p
 python synthesis.py \
 --use_gpu=1 \
 --alpha=1.0 \
---checkpoint='./checkpoint/fastspeech/step-120000' \
+--checkpoint=${CHECKPOINTPATH} \
 --config='configs/ljspeech.yaml' \
---config_clarine='../clarinet/configs/config.yaml' \
---checkpoint_clarinet='../clarinet/checkpoint/step-500000' \
---output='./synthesis' \
+--output=${OUTPUTPATH} \
+--vocoder='griffinlim' \
 ```
 
-We use Clarinet to synthesis wav, so it necessary for you to prepare a pre-trained [Clarinet checkpoint](https://paddlespeech.bj.bcebos.com/Parakeet/clarinet_ljspeech_ckpt_1.0.zip).
+We currently support two vocoders, ``griffinlim`` and ``waveflow``. You can set ``--vocoder`` to use one of them. If you want to use ``waveflow`` as your vocoder, you need to set ``--config_vocoder`` and ``--checkpoint_vocoder`` which are the path of the config and checkpoint of vocoder. You can download the pretrain model of ``waveflow`` from [here](https://github.com/PaddlePaddle/Parakeet#vocoders).
 
 Or you can run the script file directly.
 
@@ -141,3 +140,5 @@ sh synthesis.sh
 For more help on arguments
 
 ``python synthesis.py --help``.
+
+Then you can find the synthesized audio files in ``${OUTPUTPATH}/samples``.

examples/fastspeech/alignments/get_alignments.py

@@ -27,7 +27,6 @@ from collections import OrderedDict
 import paddle.fluid as fluid
 import paddle.fluid.dygraph as dg
 from parakeet.models.transformer_tts.utils import *
-from parakeet import audio
 from parakeet.models.transformer_tts import TransformerTTS
 from parakeet.models.fastspeech.utils import get_alignment
 from parakeet.utils import io
@@ -78,25 +77,6 @@ def alignments(args):
             header=None,
             quoting=csv.QUOTE_NONE,
             names=["fname", "raw_text", "normalized_text"])
-        ljspeech_processor = audio.AudioProcessor(
-            sample_rate=cfg['audio']['sr'],
-            num_mels=cfg['audio']['num_mels'],
-            min_level_db=cfg['audio']['min_level_db'],
-            ref_level_db=cfg['audio']['ref_level_db'],
-            n_fft=cfg['audio']['n_fft'],
-            win_length=cfg['audio']['win_length'],
-            hop_length=cfg['audio']['hop_length'],
-            power=cfg['audio']['power'],
-            preemphasis=cfg['audio']['preemphasis'],
-            signal_norm=True,
-            symmetric_norm=False,
-            max_norm=1.,
-            mel_fmin=0,
-            mel_fmax=None,
-            clip_norm=True,
-            griffin_lim_iters=60,
-            do_trim_silence=False,
-            sound_norm=False)
 
         pbar = tqdm(range(len(table)))
         alignments = OrderedDict()
@@ -107,11 +87,26 @@ def alignments(args):
             text = fluid.layers.unsqueeze(dg.to_variable(text), [0])
             pos_text = np.arange(1, text.shape[1] + 1)
             pos_text = fluid.layers.unsqueeze(dg.to_variable(pos_text), [0])
-            wav = ljspeech_processor.load_wav(
-                os.path.join(args.data, 'wavs', fname + ".wav"))
-            mel_input = ljspeech_processor.melspectrogram(wav).astype(
-                np.float32)
-            mel_input = np.transpose(mel_input, axes=(1, 0))
+
+            # load
+            wav, _ = librosa.load(
+                str(os.path.join(args.data, 'wavs', fname + ".wav")))
+
+            spec = librosa.stft(
+                y=wav,
+                n_fft=cfg['audio']['n_fft'],
+                win_length=cfg['audio']['win_length'],
+                hop_length=cfg['audio']['hop_length'])
+            mag = np.abs(spec)
+            mel = librosa.filters.mel(sr=cfg['audio']['sr'],
+                                      n_fft=cfg['audio']['n_fft'],
+                                      n_mels=cfg['audio']['num_mels'],
+                                      fmin=cfg['audio']['fmin'],
+                                      fmax=cfg['audio']['fmax'])
+            mel = np.matmul(mel, mag)
+            mel = np.log(np.maximum(mel, 1e-5))
+
+            mel_input = np.transpose(mel, axes=(1, 0))
             mel_input = fluid.layers.unsqueeze(dg.to_variable(mel_input), [0])
             mel_lens = mel_input.shape[1]
 
@@ -125,7 +120,7 @@ def alignments(args):
             alignment, _ = get_alignment(attn_probs, mel_lens,
                                          network_cfg['decoder_num_head'])
             alignments[fname] = alignment
-        with open(args.output + '.txt', "wb") as f:
+        with open(args.output + '.pkl', "wb") as f:
             pickle.dump(alignments, f)
 
 

examples/fastspeech/configs/ljspeech.yaml

 audio:
   num_mels: 80  #the number of mel bands when calculating mel spectrograms.
-  n_fft: 2048 #the number of fft components.
+  n_fft: 1024 #the number of fft components.
   sr: 22050 #the sampling rate of audio data file.
   hop_length: 256 #the number of samples to advance between frames.
   win_length: 1024 #the length (width) of the window function.
+  preemphasis: 0.97
   power: 1.2 #the power to raise before griffin-lim.
+  fmin: 0
+  fmax: 8000
 
 network:
   encoder_n_layer: 6 #the number of FFT Block in encoder.

examples/fastspeech/data.py

@@ -42,12 +42,7 @@ class LJSpeechLoader:
 
         LJSPEECH_ROOT = Path(data_path)
         metadata = LJSpeechMetaData(LJSPEECH_ROOT, alignments_path)
-        transformer = LJSpeech(
-            sr=config['sr'],
-            n_fft=config['n_fft'],
-            num_mels=config['num_mels'],
-            win_length=config['win_length'],
-            hop_length=config['hop_length'])
+        transformer = LJSpeech(config)
         dataset = TransformDataset(metadata, transformer)
         dataset = CacheDataset(dataset)
 
@@ -96,18 +91,16 @@ class LJSpeechMetaData(DatasetMixin):
 
 
 class LJSpeech(object):
-    def __init__(self,
-                 sr=22050,
-                 n_fft=2048,
-                 num_mels=80,
-                 win_length=1024,
-                 hop_length=256):
+    def __init__(self, cfg):
         super(LJSpeech, self).__init__()
-        self.sr = sr
-        self.n_fft = n_fft
-        self.num_mels = num_mels
-        self.win_length = win_length
-        self.hop_length = hop_length
+        self.sr = cfg['sr']
+        self.n_fft = cfg['n_fft']
+        self.num_mels = cfg['num_mels']
+        self.win_length = cfg['win_length']
+        self.hop_length = cfg['hop_length']
+        self.preemphasis = cfg['preemphasis']
+        self.fmin = cfg['fmin']
+        self.fmax = cfg['fmax']
 
     def __call__(self, metadatum):
         """All the code for generating an Example from a metadatum. If you want a 
@@ -125,7 +118,11 @@ class LJSpeech(object):
             win_length=self.win_length,
             hop_length=self.hop_length)
         mag = np.abs(spec)
-        mel = librosa.filters.mel(self.sr, self.n_fft, n_mels=self.num_mels)
+        mel = librosa.filters.mel(self.sr,
+                                  self.n_fft,
+                                  n_mels=self.num_mels,
+                                  fmin=self.fmin,
+                                  fmax=self.fmax)
         mel = np.matmul(mel, mag)
         mel = np.log(np.maximum(mel, 1e-5))
         phonemes = np.array(

examples/fastspeech/synthesis.py

@@ -40,7 +40,7 @@ def add_config_options_to_parser(parser):
         "--vocoder",
         type=str,
         default="griffinlim",
-        choices=['griffinlim', 'clarinet', 'waveflow'],
+        choices=['griffinlim', 'waveflow'],
         help="vocoder method")
     parser.add_argument(
         "--config_vocoder", type=str, help="path of the vocoder config file")
@@ -98,24 +98,13 @@ def synthesis(text_input, args):
 
     if args.vocoder == 'griffinlim':
         #synthesis use griffin-lim
-        wav = synthesis_with_griffinlim(
-            mel_output_postnet,
-            sr=cfg['audio']['sr'],
-            n_fft=cfg['audio']['n_fft'],
-            num_mels=cfg['audio']['num_mels'],
-            power=cfg['audio']['power'],
-            hop_length=cfg['audio']['hop_length'],
-            win_length=cfg['audio']['win_length'])
-    elif args.vocoder == 'clarinet':
-        # synthesis use clarinet
-        wav = synthesis_with_clarinet(mel_output_postnet, args.config_vocoder,
-                                      args.checkpoint_vocoder, place)
+        wav = synthesis_with_griffinlim(mel_output_postnet, cfg['audio'])
     elif args.vocoder == 'waveflow':
         wav = synthesis_with_waveflow(mel_output_postnet, args,
                                       args.checkpoint_vocoder, place)
     else:
         print(
-            'vocoder error, we only support griffinlim, clarinet and waveflow, but recevied %s.'
+            'vocoder error, we only support griffinlim and waveflow, but recevied %s.'
             % args.vocoder)
 
     writer.add_audio(text_input + '(' + args.vocoder + ')', wav, 0,
@@ -130,105 +119,34 @@ def synthesis(text_input, args):
     writer.close()
 
 
-def synthesis_with_griffinlim(mel_output, sr, n_fft, num_mels, power,
-                              hop_length, win_length):
+def synthesis_with_griffinlim(mel_output, cfg):
     mel_output = fluid.layers.transpose(
         fluid.layers.squeeze(mel_output, [0]), [1, 0])
     mel_output = np.exp(mel_output.numpy())
-    basis = librosa.filters.mel(sr, n_fft, num_mels)
+    basis = librosa.filters.mel(cfg['sr'],
+                                cfg['n_fft'],
+                                cfg['num_mels'],
+                                fmin=cfg['fmin'],
+                                fmax=cfg['fmax'])
     inv_basis = np.linalg.pinv(basis)
     spec = np.maximum(1e-10, np.dot(inv_basis, mel_output))
 
     wav = librosa.core.griffinlim(
-        spec**power, hop_length=hop_length, win_length=win_length)
+        spec**cfg['power'],
+        hop_length=cfg['hop_length'],
+        win_length=cfg['win_length'])
 
     return wav
 
 
-def synthesis_with_clarinet(mel_output, config_path, checkpoint, place):
-    mel_spectrogram = np.exp(mel_output.numpy())
-    with open(config_path, 'rt') as f:
-        config = yaml.safe_load(f)
-
-    data_config = config["data"]
-    n_mels = data_config["n_mels"]
-
-    teacher_config = config["teacher"]
-    n_loop = teacher_config["n_loop"]
-    n_layer = teacher_config["n_layer"]
-    filter_size = teacher_config["filter_size"]
-
-    # only batch=1 for validation is enabled
-
-    fluid.enable_dygraph(place)
-    # conditioner(upsampling net)
-    conditioner_config = config["conditioner"]
-    upsampling_factors = conditioner_config["upsampling_factors"]
-    upsample_net = UpsampleNet(upscale_factors=upsampling_factors)
-    freeze(upsample_net)
-
-    residual_channels = teacher_config["residual_channels"]
-    loss_type = teacher_config["loss_type"]
-    output_dim = teacher_config["output_dim"]
-    log_scale_min = teacher_config["log_scale_min"]
-    assert loss_type == "mog" and output_dim == 3, \
-        "the teacher wavenet should be a wavenet with single gaussian output"
-
-    teacher = WaveNet(n_loop, n_layer, residual_channels, output_dim, n_mels,
-                      filter_size, loss_type, log_scale_min)
-    # load & freeze upsample_net & teacher
-    freeze(teacher)
-
-    student_config = config["student"]
-    n_loops = student_config["n_loops"]
-    n_layers = student_config["n_layers"]
-    student_residual_channels = student_config["residual_channels"]
-    student_filter_size = student_config["filter_size"]
-    student_log_scale_min = student_config["log_scale_min"]
-    student = ParallelWaveNet(n_loops, n_layers, student_residual_channels,
-                              n_mels, student_filter_size)
-
-    stft_config = config["stft"]
-    stft = STFT(
-        n_fft=stft_config["n_fft"],
-        hop_length=stft_config["hop_length"],
-        win_length=stft_config["win_length"])
-
-    lmd = config["loss"]["lmd"]
-    model = Clarinet(upsample_net, teacher, student, stft,
-                     student_log_scale_min, lmd)
-    io.load_parameters(model=model, checkpoint_path=checkpoint)
-
-    if not os.path.exists(args.output):
-        os.makedirs(args.output)
-    model.eval()
-
-    # Rescale mel_spectrogram.
-    min_level, ref_level = 1e-5, 20  # hard code it
-    mel_spectrogram = 20 * np.log10(np.maximum(min_level, mel_spectrogram))
-    mel_spectrogram = mel_spectrogram - ref_level
-    mel_spectrogram = np.clip((mel_spectrogram + 100) / 100, 0, 1)
-
-    mel_spectrogram = dg.to_variable(mel_spectrogram)
-    mel_spectrogram = fluid.layers.transpose(mel_spectrogram, [0, 2, 1])
-
-    wav_var = model.synthesis(mel_spectrogram)
-    wav_np = wav_var.numpy()[0]
-
-    return wav_np
-
-
 def synthesis_with_waveflow(mel_output, args, checkpoint, place):
-    #mel_output = np.exp(mel_output.numpy())
-    mel_output = mel_output.numpy()
 
     fluid.enable_dygraph(place)
     args.config = args.config_vocoder
     args.use_fp16 = False
     config = io.add_yaml_config_to_args(args)
 
-    mel_spectrogram = dg.to_variable(mel_output)
-    mel_spectrogram = fluid.layers.transpose(mel_spectrogram, [0, 2, 1])
+    mel_spectrogram = fluid.layers.transpose(mel_output, [0, 2, 1])
 
     # Build model.
     waveflow = WaveFlowModule(config)
@@ -247,5 +165,6 @@ if __name__ == '__main__':
     add_config_options_to_parser(parser)
     args = parser.parse_args()
     pprint(vars(args))
-    synthesis("Simple as this proposition is, it is necessary to be stated,",
+    synthesis(
+        "Don't argue with the people of strong determination, because they may change the fact!",
         args)

examples/fastspeech/synthesis.sh

@@ -4,15 +4,12 @@ CUDA_VISIBLE_DEVICES=0 \
 python -u synthesis.py \
 --use_gpu=1 \
 --alpha=1.0 \
---checkpoint='./checkpoint/fastspeech1024/step-160000' \
+--checkpoint='./checkpoint/fastspeech/step-162000' \
 --config='configs/ljspeech.yaml' \
 --output='./synthesis' \
 --vocoder='waveflow' \
---config_vocoder='../waveflow/checkpoint/waveflow_res64_ljspeech_ckpt_1.0/waveflow_ljspeech.yaml' \
---checkpoint_vocoder='../waveflow/checkpoint/waveflow_res64_ljspeech_ckpt_1.0/step-3020000' \
-#--vocoder='clarinet' \
-#--config_vocoder='../clarinet/configs/clarinet_ljspeech.yaml' \
-#--checkpoint_vocoder='../clarinet/checkpoint/step-500000' \
+--config_vocoder='../waveflow/checkpoint/waveflow_res128_ljspeech_ckpt_1.0/waveflow_ljspeech.yaml' \
+--checkpoint_vocoder='../waveflow/checkpoint/waveflow_res128_ljspeech_ckpt_1.0/step-2000000' \
 
 
 

examples/fastspeech/train.sh

@@ -3,7 +3,7 @@ export CUDA_VISIBLE_DEVICES=0
 python -u train.py \
 --use_gpu=1 \
 --data='../../dataset/LJSpeech-1.1' \
---alignments_path='./alignments/alignments.txt' \
+--alignments_path='./alignments/alignments.pkl' \
 --output='./experiment' \
 --config='configs/ljspeech.yaml' \
 #--checkpoint='./checkpoint/fastspeech/step-120000' \

examples/transformer_tts/README.md

@@ -56,7 +56,7 @@ TransformerTTS model can be trained by running ``train_transformer.py``.
 python train_transformer.py \
 --use_gpu=1 \
 --data=${DATAPATH} \
---output='./experiment' \
+--output=${OUTPUTPATH} \
 --config='configs/ljspeech.yaml' \
 ```
 
@@ -73,7 +73,7 @@ CUDA_VISIBLE_DEVICES=0,1,2,3
 python -m paddle.distributed.launch --selected_gpus=0,1,2,3 --log_dir ./mylog train_transformer.py \
 --use_gpu=1 \
 --data=${DATAPATH} \
---output='./experiment' \
+--output=${OUTPUTPATH} \
 --config='configs/ljspeech.yaml' \
 ```
 
@@ -85,61 +85,28 @@ For more help on arguments
 
 ``python train_transformer.py --help``.
 
-## Train Vocoder
-
-Vocoder model can be trained by running ``train_vocoder.py``.
-
-```bash
-python train_vocoder.py \
---use_gpu=1 \
---data=${DATAPATH} \
---output='./vocoder' \
---config='configs/ljspeech.yaml' \
-```
-
-Or you can run the script file directly.
-
-```bash
-sh train_vocoder.sh
-```
-
-If you want to train on multiple GPUs, you must start training in the following way.
-
-```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3
-python -m paddle.distributed.launch --selected_gpus=0,1,2,3 --log_dir ./mylog train_vocoder.py \
---use_gpu=1 \
---data=${DATAPATH} \
---output='./vocoder' \
---config='configs/ljspeech.yaml' \
-```
-
-If you wish to resume from an existing model, See [Saving-&-Loading](#Saving-&-Loading) for details of checkpoint loading.
-
-For more help on arguments
-
-``python train_vocoder.py --help``.
-
 ## Synthesis
 
-After training the TransformerTTS and vocoder model, audio can be synthesized by running ``synthesis.py``.
+After training the TransformerTTS, audio can be synthesized by running ``synthesis.py``.
 
 ```bash
 python synthesis.py \
---max_len=300 \
---use_gpu=1 \
---output='./synthesis' \
+--use_gpu=0 \
+--output=${OUTPUTPATH} \
 --config='configs/ljspeech.yaml' \
---checkpoint_transformer='./checkpoint/transformer/step-120000' \
---checkpoint_vocoder='./checkpoint/vocoder/step-100000' \
+--checkpoint_transformer=${CHECKPOINTPATH} \
+--vocoder='griffinlim' \
 ```
 
+We currently support two vocoders, ``griffinlim`` and ``waveflow``. You can set ``--vocoder`` to use one of them. If you want to use ``waveflow`` as your vocoder, you need to set ``--config_vocoder`` and ``--checkpoint_vocoder`` which are the path of the config and checkpoint of vocoder. You can download the pretrain model of ``waveflow`` from [here](https://github.com/PaddlePaddle/Parakeet#vocoders).
+
 Or you can run the script file directly.
 
 ```bash
 sh synthesis.sh
 ```
-
 For more help on arguments
 
 ``python synthesis.py --help``.
+
+Then you can find the synthesized audio files in ``${OUTPUTPATH}/samples``.

examples/transformer_tts/configs/ljspeech.yaml

 audio:
   num_mels: 80
-  n_fft: 2048
+  n_fft: 1024
   sr: 22050
   preemphasis: 0.97
-  hop_length: 256 #275
-  win_length: 1024 #1102
+  hop_length: 256
+  win_length: 1024
   power: 1.2
-  min_level_db: -100
-  ref_level_db: 20
+  fmin: 0
+  fmax: 8000
 
 network:
   hidden_size: 256
@@ -17,7 +17,7 @@ network:
   decoder_num_head: 4
   decoder_n_layers: 3
   outputs_per_step: 1
-  stop_token: False
+  stop_loss_weight: 8
 
 vocoder:
   hidden_size: 256

examples/transformer_tts/data.py

@@ -19,7 +19,6 @@ import csv
 
 from paddle import fluid
 from parakeet import g2p
-from parakeet import audio
 from parakeet.data.sampler import *
 from parakeet.data.datacargo import DataCargo
 from parakeet.data.batch import TextIDBatcher, SpecBatcher
@@ -98,25 +97,14 @@ class LJSpeech(object):
     def __init__(self, config):
         super(LJSpeech, self).__init__()
         self.config = config
-        self._ljspeech_processor = audio.AudioProcessor(
-            sample_rate=config['sr'],
-            num_mels=config['num_mels'],
-            min_level_db=config['min_level_db'],
-            ref_level_db=config['ref_level_db'],
-            n_fft=config['n_fft'],
-            win_length=config['win_length'],
-            hop_length=config['hop_length'],
-            power=config['power'],
-            preemphasis=config['preemphasis'],
-            signal_norm=True,
-            symmetric_norm=False,
-            max_norm=1.,
-            mel_fmin=0,
-            mel_fmax=None,
-            clip_norm=True,
-            griffin_lim_iters=60,
-            do_trim_silence=False,
-            sound_norm=False)
+        self.sr = config['sr']
+        self.n_mels = config['num_mels']
+        self.preemphasis = config['preemphasis']
+        self.n_fft = config['n_fft']
+        self.win_length = config['win_length']
+        self.hop_length = config['hop_length']
+        self.fmin = config['fmin']
+        self.fmax = config['fmax']
 
     def __call__(self, metadatum):
         """All the code for generating an Example from a metadatum. If you want a 
@@ -127,14 +115,26 @@ class LJSpeech(object):
         """
         fname, raw_text, normalized_text = metadatum
 
-        # load -> trim -> preemphasis -> stft -> magnitude -> mel_scale -> logscale -> normalize
-        wav = self._ljspeech_processor.load_wav(str(fname))
-        mag = self._ljspeech_processor.spectrogram(wav).astype(np.float32)
-        mel = self._ljspeech_processor.melspectrogram(wav).astype(np.float32)
-        phonemes = np.array(
+        # load
+        wav, _ = librosa.load(str(fname))
+
+        spec = librosa.stft(
+            y=wav,
+            n_fft=self.n_fft,
+            win_length=self.win_length,
+            hop_length=self.hop_length)
+        mag = np.abs(spec)
+        mel = librosa.filters.mel(sr=self.sr,
+                                  n_fft=self.n_fft,
+                                  n_mels=self.n_mels,
+                                  fmin=self.fmin,
+                                  fmax=self.fmax)
+        mel = np.matmul(mel, mag)
+        mel = np.log(np.maximum(mel, 1e-5))
+
+        characters = np.array(
             g2p.en.text_to_sequence(normalized_text), dtype=np.int64)
-        return (mag, mel, phonemes
-                )  # maybe we need to implement it as a map in the future
+        return (mag, mel, characters)
 
 
 def batch_examples(batch):
@@ -144,6 +144,7 @@ def batch_examples(batch):
     text_lens = []
     pos_texts = []
     pos_mels = []
+    stop_tokens = []
     for data in batch:
         _, mel, text = data
         mel_inputs.append(
@@ -155,6 +156,8 @@ def batch_examples(batch):
         pos_mels.append(np.arange(1, mel.shape[1] + 1))
         mels.append(mel)
         texts.append(text)
+        stop_token = np.append(np.zeros([mel.shape[1] - 1], np.float32), 1.0)
+        stop_tokens.append(stop_token)
 
     # Sort by text_len in descending order
     texts = [
@@ -182,18 +185,24 @@ def batch_examples(batch):
         for i, _ in sorted(
             zip(pos_mels, text_lens), key=lambda x: x[1], reverse=True)
     ]
+    stop_tokens = [
+        i
+        for i, _ in sorted(
+            zip(stop_tokens, text_lens), key=lambda x: x[1], reverse=True)
+    ]
     text_lens = sorted(text_lens, reverse=True)
 
     # Pad sequence with largest len of the batch
     texts = TextIDBatcher(pad_id=0)(texts)  #(B, T)
     pos_texts = TextIDBatcher(pad_id=0)(pos_texts)  #(B,T)
     pos_mels = TextIDBatcher(pad_id=0)(pos_mels)  #(B,T)
+    stop_tokens = TextIDBatcher(pad_id=1, dtype=np.float32)(pos_mels)
     mels = np.transpose(
         SpecBatcher(pad_value=0.)(mels), axes=(0, 2, 1))  #(B,T,num_mels)
     mel_inputs = np.transpose(
         SpecBatcher(pad_value=0.)(mel_inputs), axes=(0, 2, 1))  #(B,T,num_mels)
 
-    return (texts, mels, mel_inputs, pos_texts, pos_mels)
+    return (texts, mels, mel_inputs, pos_texts, pos_mels, stop_tokens)
 
 
 def batch_examples_vocoder(batch):

examples/transformer_tts/synthesis.py

@@ -25,23 +25,25 @@ import paddle.fluid as fluid
 import paddle.fluid.dygraph as dg
 from parakeet.g2p.en import text_to_sequence
 from parakeet.models.transformer_tts.utils import *
-from parakeet import audio
-from parakeet.models.transformer_tts import Vocoder
 from parakeet.models.transformer_tts import TransformerTTS
-from parakeet.modules import weight_norm
 from parakeet.models.waveflow import WaveFlowModule
 from parakeet.modules.weight_norm import WeightNormWrapper
-from parakeet.models.wavenet import UpsampleNet, WaveNet, ConditionalWavenet
 from parakeet.utils import io
 
 
 def add_config_options_to_parser(parser):
     parser.add_argument("--config", type=str, help="path of the config file")
     parser.add_argument("--use_gpu", type=int, default=0, help="device to use")
+    parser.add_argument(
+        "--stop_threshold",
+        type=float,
+        default=0.5,
+        help="The threshold of stop token which indicates the time step should stop generate spectrum or not."
+    )
     parser.add_argument(
         "--max_len",
         type=int,
-        default=200,
+        default=1000,
         help="The max length of audio when synthsis.")
 
     parser.add_argument(
@@ -52,7 +54,7 @@ def add_config_options_to_parser(parser):
         "--vocoder",
         type=str,
         default="griffinlim",
-        choices=['griffinlim', 'wavenet', 'waveflow'],
+        choices=['griffinlim', 'waveflow'],
         help="vocoder method")
     parser.add_argument(
         "--config_vocoder", type=str, help="path of the vocoder config file")
@@ -102,13 +104,14 @@ def synthesis(text_input, args):
     pos_text = fluid.layers.unsqueeze(
         dg.to_variable(pos_text).astype(np.int64), [0])
 
-    pbar = tqdm(range(args.max_len))
-    for i in pbar:
+    for i in range(args.max_len):
         pos_mel = np.arange(1, mel_input.shape[1] + 1)
         pos_mel = fluid.layers.unsqueeze(
             dg.to_variable(pos_mel).astype(np.int64), [0])
         mel_pred, postnet_pred, attn_probs, stop_preds, attn_enc, attn_dec = model(
             text, mel_input, pos_text, pos_mel)
+        if stop_preds.numpy()[0, -1] > args.stop_threshold:
+            break
         mel_input = fluid.layers.concat(
             [mel_input, postnet_pred[:, -1:, :]], axis=1)
     global_step = 0
@@ -121,40 +124,16 @@ def synthesis(text_input, args):
                 i * 4 + j,
                 dataformats="HWC")
 
-    _ljspeech_processor = audio.AudioProcessor(
-        sample_rate=cfg['audio']['sr'],
-        num_mels=cfg['audio']['num_mels'],
-        min_level_db=cfg['audio']['min_level_db'],
-        ref_level_db=cfg['audio']['ref_level_db'],
-        n_fft=cfg['audio']['n_fft'],
-        win_length=cfg['audio']['win_length'],
-        hop_length=cfg['audio']['hop_length'],
-        power=cfg['audio']['power'],
-        preemphasis=cfg['audio']['preemphasis'],
-        signal_norm=True,
-        symmetric_norm=False,
-        max_norm=1.,
-        mel_fmin=0,
-        mel_fmax=8000,
-        clip_norm=True,
-        griffin_lim_iters=60,
-        do_trim_silence=False,
-        sound_norm=False)
-
     if args.vocoder == 'griffinlim':
         #synthesis use griffin-lim
-        wav = synthesis_with_griffinlim(postnet_pred, _ljspeech_processor)
-    elif args.vocoder == 'wavenet':
-        # synthesis use wavenet
-        wav = synthesis_with_wavenet(postnet_pred, args)
+        wav = synthesis_with_griffinlim(postnet_pred, cfg['audio'])
     elif args.vocoder == 'waveflow':
         # synthesis use waveflow
         wav = synthesis_with_waveflow(postnet_pred, args,
-                                      args.checkpoint_vocoder,
-                                      _ljspeech_processor, place)
+                                      args.checkpoint_vocoder, place)
     else:
         print(
-            'vocoder error, we only support griffinlim, cbhg and waveflow, but recevied %s.'
+            'vocoder error, we only support griffinlim and waveflow, but recevied %s.'
             % args.vocoder)
 
     writer.add_audio(text_input + '(' + args.vocoder + ')', wav, 0,
@@ -169,91 +148,42 @@ def synthesis(text_input, args):
     writer.close()
 
 
-def synthesis_with_griffinlim(mel_output, _ljspeech_processor):
+def synthesis_with_griffinlim(mel_output, cfg):
     # synthesis with griffin-lim
     mel_output = fluid.layers.transpose(
         fluid.layers.squeeze(mel_output, [0]), [1, 0])
     mel_output = np.exp(mel_output.numpy())
-    basis = librosa.filters.mel(22050, 1024, 80, fmin=0, fmax=8000)
+    basis = librosa.filters.mel(cfg['sr'],
+                                cfg['n_fft'],
+                                cfg['num_mels'],
+                                fmin=cfg['fmin'],
+                                fmax=cfg['fmax'])
     inv_basis = np.linalg.pinv(basis)
     spec = np.maximum(1e-10, np.dot(inv_basis, mel_output))
 
-    wav = librosa.core.griffinlim(spec**1.2, hop_length=256, win_length=1024)
-
-    return wav
-
-
-def synthesis_with_wavenet(mel_output, args):
-    with open(args.config_vocoder, 'rt') as f:
-        config = yaml.safe_load(f)
-    n_mels = config["data"]["n_mels"]
-    model_config = config["model"]
-    filter_size = model_config["filter_size"]
-    upsampling_factors = model_config["upsampling_factors"]
-    encoder = UpsampleNet(upsampling_factors)
-
-    n_loop = model_config["n_loop"]
-    n_layer = model_config["n_layer"]
-    residual_channels = model_config["residual_channels"]
-    output_dim = model_config["output_dim"]
-    loss_type = model_config["loss_type"]
-    log_scale_min = model_config["log_scale_min"]
-    decoder = WaveNet(n_loop, n_layer, residual_channels, output_dim, n_mels,
-                      filter_size, loss_type, log_scale_min)
-
-    model = ConditionalWavenet(encoder, decoder)
-
-    # load model parameters
-    iteration = io.load_parameters(
-        model, checkpoint_path=args.checkpoint_vocoder)
-
-    for layer in model.sublayers():
-        if isinstance(layer, WeightNormWrapper):
-            layer.remove_weight_norm()
-    mel_output = fluid.layers.transpose(mel_output, [0, 2, 1])
-    wav = model.synthesis(mel_output)
-    return wav.numpy()[0]
-
+    wav = librosa.core.griffinlim(
+        spec**cfg['power'],
+        hop_length=cfg['hop_length'],
+        win_length=cfg['win_length'])
 
-def synthesis_with_cbhg(mel_output, _ljspeech_processor, cfg):
-    with fluid.unique_name.guard():
-        model_vocoder = Vocoder(
-            cfg['train']['batch_size'], cfg['vocoder']['hidden_size'],
-            cfg['audio']['num_mels'], cfg['audio']['n_fft'])
-        # Load parameters.
-        global_step = io.load_parameters(
-            model=model_vocoder, checkpoint_path=args.checkpoint_vocoder)
-        model_vocoder.eval()
-    mag_pred = model_vocoder(mel_output)
-    # synthesis with cbhg
-    wav = _ljspeech_processor.inv_spectrogram(
-        fluid.layers.transpose(fluid.layers.squeeze(mag_pred, [0]), [1, 0])
-        .numpy())
     return wav
 
 
-def synthesis_with_waveflow(mel_output, args, checkpoint, _ljspeech_processor,
-                            place):
-    mel_output = fluid.layers.transpose(
-        fluid.layers.squeeze(mel_output, [0]), [1, 0])
-    mel_output = mel_output.numpy()
-    #mel_output = (mel_output - mel_output.min())/(mel_output.max() - mel_output.min())
-    #mel_output = 5 * mel_output - 4
-    #mel_output = np.log(10) * mel_output
-
+def synthesis_with_waveflow(mel_output, args, checkpoint, place):
     fluid.enable_dygraph(place)
     args.config = args.config_vocoder
     args.use_fp16 = False
     config = io.add_yaml_config_to_args(args)
 
-    mel_spectrogram = dg.to_variable(mel_output)
+    mel_spectrogram = fluid.layers.transpose(
+        fluid.layers.squeeze(mel_output, [0]), [1, 0])
     mel_spectrogram = fluid.layers.unsqueeze(mel_spectrogram, [0])
 
     # Build model.
     waveflow = WaveFlowModule(config)
     io.load_parameters(model=waveflow, checkpoint_path=checkpoint)
     for layer in waveflow.sublayers():
-        if isinstance(layer, weight_norm.WeightNormWrapper):
+        if isinstance(layer, WeightNormWrapper):
             layer.remove_weight_norm()
 
     # Run model inference.
@@ -268,5 +198,5 @@ if __name__ == '__main__':
     # Print the whole config setting.
     pprint(vars(args))
     synthesis(
-        "Life was like a box of chocolates, you never know what you're gonna get.",
+        "Life was like a box of chocolates, you never know what you're gonna get.",
         args)

examples/transformer_tts/synthesis.sh

@@ -2,20 +2,13 @@
 # train model
 CUDA_VISIBLE_DEVICES=0 \
 python -u synthesis.py \
---max_len=400 \
 --use_gpu=0 \
 --output='./synthesis' \
 --config='configs/ljspeech.yaml' \
 --checkpoint_transformer='./checkpoint/transformer/step-120000' \
---vocoder='wavenet' \
---config_vocoder='../wavenet/config.yaml' \
---checkpoint_vocoder='../wavenet/step-2450000' \
-#--vocoder='waveflow' \
-#--config_vocoder='../waveflow/checkpoint/waveflow_res64_ljspeech_ckpt_1.0/waveflow_ljspeech.yaml' \
-#--checkpoint_vocoder='../waveflow/checkpoint/waveflow_res64_ljspeech_ckpt_1.0/step-3020000' \
-#--vocoder='cbhg' \
-#--config_vocoder='configs/ljspeech.yaml' \
-#--checkpoint_vocoder='checkpoint/cbhg/step-100000' \
+--vocoder='waveflow' \
+--config_vocoder='../waveflow/checkpoint/waveflow_res128_ljspeech_ckpt_1.0/waveflow_ljspeech.yaml' \
+--checkpoint_vocoder='../waveflow/checkpoint/waveflow_res128_ljspeech_ckpt_1.0/step-2000000' \
 
 if [ $? -ne 0 ]; then
     echo "Failed in training!"

examples/transformer_tts/train_transformer.py

@@ -115,7 +115,7 @@ def main(args):
             iterator = iter(tqdm(reader))
             batch = next(iterator)
 
-        character, mel, mel_input, pos_text, pos_mel = batch
+        character, mel, mel_input, pos_text, pos_mel, stop_tokens = batch
 
         mel_pred, postnet_pred, attn_probs, stop_preds, attn_enc, attn_dec = model(
             character, mel_input, pos_text, pos_mel)
@@ -126,10 +126,8 @@ def main(args):
             layers.abs(layers.elementwise_sub(postnet_pred, mel)))
         loss = mel_loss + post_mel_loss
 
-        # Note: When used stop token loss the learning did not work.
-        if cfg['network']['stop_token']:
-            label = (pos_mel == 0).astype(np.float32)
-            stop_loss = cross_entropy(stop_preds, label)
+        stop_loss = cross_entropy(
+            stop_preds, stop_tokens, weight=cfg['network']['stop_loss_weight'])
         loss = loss + stop_loss
 
         if local_rank == 0:
@@ -138,7 +136,6 @@ def main(args):
                 'post_mel_loss': post_mel_loss.numpy()
             }, global_step)
 
-            if cfg['network']['stop_token']:
             writer.add_scalar('stop_loss', stop_loss.numpy(), global_step)
 
             if parallel:

parakeet/models/fastspeech/length_regulator.py

@@ -37,13 +37,12 @@ class LengthRegulator(dg.Layer):
             filter_size=filter_size,
             dropout=dropout)
 
-    def LR(self, x, duration_predictor_output, alpha=1.0):
+    def LR(self, x, duration_predictor_output):
         output = []
         batch_size = x.shape[0]
         for i in range(batch_size):
             output.append(
-                self.expand(x[i:i + 1], duration_predictor_output[i:i + 1],
-                            alpha))
+                self.expand(x[i:i + 1], duration_predictor_output[i:i + 1]))
         output = self.pad(output)
         return output
 
@@ -58,7 +57,7 @@ class LengthRegulator(dg.Layer):
         out_padded = layers.stack(out_list)
         return out_padded
 
-    def expand(self, batch, predicted, alpha):
+    def expand(self, batch, predicted):
         out = []
         time_steps = batch.shape[1]
         fertilities = predicted.numpy()
@@ -92,8 +91,9 @@ class LengthRegulator(dg.Layer):
             output = self.LR(x, target)
             return output, duration_predictor_output
         else:
-            duration_predictor_output = layers.round(duration_predictor_output)
-            output = self.LR(x, duration_predictor_output, alpha)
+            duration_predictor_output = duration_predictor_output * alpha
+            duration_predictor_output = layers.ceil(duration_predictor_output)
+            output = self.LR(x, duration_predictor_output)
             mel_pos = dg.to_variable(np.arange(1, output.shape[1] + 1)).astype(
                 np.int64)
             mel_pos = layers.unsqueeze(mel_pos, [0])

parakeet/models/transformer_tts/utils.py

@@ -93,9 +93,9 @@ def guided_attention(N, T, g=0.2):
     return W
 
 
-def cross_entropy(input, label, position_weight=1.0, epsilon=1e-30):
+def cross_entropy(input, label, weight=1.0, epsilon=1e-30):
     output = -1 * label * layers.log(input + epsilon) - (
         1 - label) * layers.log(1 - input + epsilon)
-    output = output * (label * (position_weight - 1) + 1)
+    output = output * (label * (weight - 1) + 1)
 
-    return layers.reduce_sum(output, dim=[0, 1])
+    return layers.reduce_mean(output, dim=[0, 1])