tf bacend for synthesis

utils/synthesis.py

@@ -1,3 +1,7 @@
+import pkg_resources
+installed = {pkg.key for pkg in pkg_resources.working_set}
+if 'tensorflow' in installed:
+    import tensorflow as tf
 import torch
 import numpy as np
 from .text import text_to_sequence, phoneme_to_sequence
@@ -14,23 +18,32 @@ def text_to_seqvec(text, CONFIG, use_cuda):
             dtype=np.int32)
     else:
         seq = np.asarray(text_to_sequence(text, text_cleaner, tp=CONFIG.characters if 'characters' in CONFIG.keys() else None), dtype=np.int32)
-    # torch tensor
+    return seq
-    chars_var = torch.from_numpy(seq).unsqueeze(0)
+
-    if use_cuda:
+
-        chars_var = chars_var.cuda()
+def numpy_to_torch(np_array, dtype, cuda=False):
-    return chars_var.long()
+    if np_array is None:
+        return None
+    tensor = torch.Tensor(np_array, dtype=dtype) 
+    if cuda:
+        return tensor.cuda() 
+    return tensor
+
+
+def numpy_to_tf(np_array, dtype):
+    if np_array is None:
+        return None
+    tensor = tf.convert_to_tensor(np_array, dtype=dtype)
+    return tensor
 
 
 def compute_style_mel(style_wav, ap, use_cuda):
-    print(style_wav)
+    style_mel = ap.melspectrogram(
-    style_mel = torch.FloatTensor(ap.melspectrogram(
+        ap.load_wav(style_wav)).expand_dims(0)
-        ap.load_wav(style_wav))).unsqueeze(0)
-    if use_cuda:
-        return style_mel.cuda()
     return style_mel
 
 
-def run_model(model, inputs, CONFIG, truncated, speaker_id=None, style_mel=None):
+def run_model_torch(model, inputs, CONFIG, truncated, speaker_id=None, style_mel=None):
     if CONFIG.use_gst:
         decoder_output, postnet_output, alignments, stop_tokens = model.inference(
             inputs, style_mel=style_mel, speaker_ids=speaker_id)
@@ -44,11 +57,31 @@ def run_model(model, inputs, CONFIG, truncated, speaker_id=None, style_mel=None)
     return decoder_output, postnet_output, alignments, stop_tokens
 
 
-def parse_outputs(postnet_output, decoder_output, alignments):
+def run_model_tf(model, inputs, CONFIG, truncated, speaker_id=None, style_mel=None):
+    if CONFIG.use_gst:
+        raise NotImplemented(' [!] GST inference not implemented for TF')
+    if truncated:
+        raise NotImplemented(' [!] Truncated inference not implemented for TF')
+    # TODO: handle multispeaker case
+    decoder_output, postnet_output, alignments, stop_tokens = model(
+        inputs, training=False)
+    return decoder_output, postnet_output, alignments, stop_tokens
+
+
+def parse_outputs_torch(postnet_output, decoder_output, alignments, stop_tokens):
     postnet_output = postnet_output[0].data.cpu().numpy()
     decoder_output = decoder_output[0].data.cpu().numpy()
     alignment = alignments[0].cpu().data.numpy()
-    return postnet_output, decoder_output, alignment
+    stop_tokens = stop_tokens[0].cpu().numpy()
+    return postnet_output, decoder_output, alignment, stop_tokens
+
+
+def parse_outputs_tf(postnet_output, decoder_output, alignments, stop_tokens):
+    postnet_output = postnet_output[0].numpy()
+    decoder_output = decoder_output[0].numpy()
+    alignment = alignments[0].numpy()
+    stop_tokens = stop_tokens[0].numpy()
+    return postnet_output, decoder_output, alignment, stop_tokens
 
 
 def trim_silence(wav, ap):
@@ -98,7 +131,8 @@ def synthesis(model,
               truncated=False,
               enable_eos_bos_chars=False, #pylint: disable=unused-argument
               use_griffin_lim=False,
-              do_trim_silence=False):
+              do_trim_silence=False,
+              backend='torch'):
     """Synthesize voice for the given text.
 
         Args:
@@ -114,6 +148,7 @@ def synthesis(model,
                 for continuous inference at long texts.
             enable_eos_bos_chars (bool): enable special chars for end of sentence and start of sentence.
             do_trim_silence (bool): trim silence after synthesis.
+            backend (str): tf or torch
     """
     # GST processing
     style_mel = None
@@ -121,15 +156,29 @@ def synthesis(model,
         style_mel = compute_style_mel(style_wav, ap, use_cuda)
     # preprocess the given text
     inputs = text_to_seqvec(text, CONFIG, use_cuda)
-    speaker_id = id_to_torch(speaker_id)
+    # pass tensors to backend
-    if speaker_id is not None and use_cuda:
+    if backend == 'torch':
-        speaker_id = speaker_id.cuda()
+        speaker_id = id_to_torch(speaker_id)
+        style_mel = numpy_to_torch(style_mel, torch.float, cuda=use_cuda)
+        inputs = numpy_to_torch(inputs, torch.long, cuda=use_cuda)
+        inputs = inputs.unsqueeze(0)
+    else:
+        # TODO: handle speaker id for tf model
+        style_mel = numpy_to_tf(style_mel, tf.float32)
+        inputs = numpy_to_tf(inputs, tf.int32)
+        inputs = tf.expand_dims(inputs, 0)
     # synthesize voice
-    decoder_output, postnet_output, alignments, stop_tokens = run_model(
+    if backend == 'torch':
-        model, inputs, CONFIG, truncated, speaker_id, style_mel)
+        decoder_output, postnet_output, alignments, stop_tokens = run_model_torch(
+            model, inputs, CONFIG, truncated, speaker_id, style_mel)
+        postnet_output, decoder_output, alignment, stop_tokens = parse_outputs_torch(
+            postnet_output, decoder_output, alignments, stop_tokens)
+    else:
+        decoder_output, postnet_output, alignments, stop_tokens = run_model_tf(
+            model, inputs, CONFIG, truncated, speaker_id, style_mel)
+        postnet_output, decoder_output, alignment, stop_tokens = parse_outputs_tf(
+            postnet_output, decoder_output, alignments, stop_tokens)
     # convert outputs to numpy
-    postnet_output, decoder_output, alignment = parse_outputs(
-        postnet_output, decoder_output, alignments)
     # plot results
     wav = None
     if use_griffin_lim:

utils/visual.py

@@ -61,7 +61,6 @@ def visualize(alignment, postnet_output, stop_tokens, text, hop_length, CONFIG,
     plt.yticks(range(len(text)), list(text))
     plt.colorbar()
     # plot stopnet predictions
-    stop_tokens = stop_tokens.squeeze().detach().to('cpu').numpy()
     plt.subplot(num_plot, 1, 2)
     plt.plot(range(len(stop_tokens)), list(stop_tokens))
     # plot postnet spectrogram