mirror of https://github.com/coqui-ai/TTS.git
update `synthesis.py` for being more generic
Eren Gölge
parent
f121b0ff5d
commit
fb9289d365
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@ -152,16 +152,6 @@ def run_model_tflite(model, inputs, CONFIG, speaker_id=None, style_mel=None):
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return decoder_output, postnet_output, None, None
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def parse_outputs_torch(postnet_output, decoder_output, alignments,
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stop_tokens):
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postnet_output = postnet_output[0].data.cpu().numpy()
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decoder_output = None if decoder_output is None else decoder_output[
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0].data.cpu().numpy()
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alignment = alignments[0].cpu().data.numpy()
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stop_tokens = None if stop_tokens is None else stop_tokens[0].cpu().numpy()
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return postnet_output, decoder_output, alignment, stop_tokens
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def parse_outputs_tf(postnet_output, decoder_output, alignments, stop_tokens):
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postnet_output = postnet_output[0].numpy()
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decoder_output = decoder_output[0].numpy()
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@ -200,8 +190,8 @@ def speaker_id_to_torch(speaker_id, cuda=False):
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def embedding_to_torch(x_vector, cuda=False):
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if x_vector is not None:
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x_vector = np.asarray(x_vector)
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x_vector = torch.from_numpy(x_vector).unsqueeze(
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0).type(torch.FloatTensor)
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x_vector = torch.from_numpy(x_vector).unsqueeze(0).type(
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torch.FloatTensor)
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if cuda:
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return x_vector.cuda()
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return x_vector
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@ -263,57 +253,59 @@ def synthesis(
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else:
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style_mel = compute_style_mel(style_wav, ap, cuda=use_cuda)
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# preprocess the given text
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inputs = text_to_seq(text, CONFIG)
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text_inputs = text_to_seq(text, CONFIG)
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# pass tensors to backend
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if backend == "torch":
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if speaker_id is not None:
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speaker_id = speaker_id_to_torch(speaker_id, cuda=use_cuda)
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if x_vector is not None:
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x_vector = embedding_to_torch(x_vector,
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cuda=use_cuda)
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x_vector = embedding_to_torch(x_vector, cuda=use_cuda)
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if not isinstance(style_mel, dict):
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style_mel = numpy_to_torch(style_mel, torch.float, cuda=use_cuda)
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inputs = numpy_to_torch(inputs, torch.long, cuda=use_cuda)
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inputs = inputs.unsqueeze(0)
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text_inputs = numpy_to_torch(text_inputs, torch.long, cuda=use_cuda)
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text_inputs = text_inputs.unsqueeze(0)
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elif backend == "tf":
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# TODO: handle speaker id for tf model
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style_mel = numpy_to_tf(style_mel, tf.float32)
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inputs = numpy_to_tf(inputs, tf.int32)
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inputs = tf.expand_dims(inputs, 0)
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text_inputs = numpy_to_tf(text_inputs, tf.int32)
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text_inputs = tf.expand_dims(text_inputs, 0)
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elif backend == "tflite":
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style_mel = numpy_to_tf(style_mel, tf.float32)
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inputs = numpy_to_tf(inputs, tf.int32)
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inputs = tf.expand_dims(inputs, 0)
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text_inputs = numpy_to_tf(text_inputs, tf.int32)
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text_inputs = tf.expand_dims(text_inputs, 0)
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# synthesize voice
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if backend == "torch":
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outputs = run_model_torch(model,
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inputs,
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text_inputs,
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speaker_id,
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style_mel,
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x_vector=x_vector)
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postnet_output, decoder_output, alignments, stop_tokens = \
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outputs['postnet_outputs'], outputs['decoder_outputs'],\
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outputs['alignments'], outputs['stop_tokens']
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postnet_output, decoder_output, alignment, stop_tokens = parse_outputs_torch(
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postnet_output, decoder_output, alignments, stop_tokens)
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model_outputs = outputs['model_outputs']
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model_outputs = model_outputs[0].data.cpu().numpy()
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elif backend == "tf":
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decoder_output, postnet_output, alignments, stop_tokens = run_model_tf(
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model, inputs, CONFIG, speaker_id, style_mel)
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postnet_output, decoder_output, alignment, stop_tokens = parse_outputs_tf(
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model, text_inputs, CONFIG, speaker_id, style_mel)
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model_outputs, decoder_output, alignment, stop_tokens = parse_outputs_tf(
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postnet_output, decoder_output, alignments, stop_tokens)
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elif backend == "tflite":
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decoder_output, postnet_output, alignment, stop_tokens = run_model_tflite(
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model, inputs, CONFIG, speaker_id, style_mel)
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postnet_output, decoder_output = parse_outputs_tflite(
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model, text_inputs, CONFIG, speaker_id, style_mel)
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model_outputs, decoder_output = parse_outputs_tflite(
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postnet_output, decoder_output)
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# convert outputs to numpy
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# plot results
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wav = None
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if use_griffin_lim:
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wav = inv_spectrogram(postnet_output, ap, CONFIG)
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wav = inv_spectrogram(model_outputs, ap, CONFIG)
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# trim silence
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if do_trim_silence:
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wav = trim_silence(wav, ap)
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return wav, alignment, decoder_output, postnet_output, stop_tokens, inputs
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return_dict = {
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'wav': wav,
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'alignments': outputs['alignments'],
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'model_outputs': model_outputs,
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'text_inputs': text_inputs
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}
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return return_dict
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