mirror of https://github.com/coqui-ai/TTS.git
Add prosody encoder params on config
This commit is contained in:
Edresson Casanova
parent
5271846d9c
commit
3a524b0597
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@ -178,7 +178,7 @@ If you don't specify any models, then it uses LJSpeech based English model.
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help="wav file(s) to condition a multi-speaker TTS model with a Speaker Encoder. You can give multiple file paths. The d_vectors is computed as their average.",
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default=None,
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)
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parser.add_argument("--gst_style", help="Wav path file for GST stylereference.", default=None)
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parser.add_argument("--gst_style", help="Wav path file for GST style reference.", default=None)
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parser.add_argument(
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"--list_speaker_idxs",
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help="List available speaker ids for the defined multi-speaker model.",
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@ -317,6 +317,7 @@ If you don't specify any models, then it uses LJSpeech based English model.
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args.speaker_idx,
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args.language_idx,
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args.speaker_wav,
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style_wav=args.gst_style,
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reference_wav=args.reference_wav,
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reference_speaker_name=args.reference_speaker_idx,
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emotion_name=args.emotion_idx,
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@ -117,7 +117,7 @@ def load_tts_samples(
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if eval_split:
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if meta_file_val:
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meta_data_eval = formatter(root_path, meta_file_val, ignored_speakers=ignored_speakers)
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meta_data_eval = [{**item, **{"language": language}} for item in meta_data_eval]
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meta_data_eval = [{**item, **{"language": language, "speech_style": speech_style}} for item in meta_data_eval]
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else:
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meta_data_eval, meta_data_train = split_dataset(meta_data_train, eval_split_max_size, eval_split_size)
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meta_data_eval_all += meta_data_eval
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@ -189,7 +189,7 @@ class Tacotron(BaseTacotron):
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encoder_outputs = self.encoder(inputs)
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if self.gst and self.use_gst:
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# B x gst_dim
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encoder_outputs = self.compute_gst(encoder_outputs, aux_input["style_mel"], aux_input["d_vectors"])
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encoder_outputs = self.compute_gst(encoder_outputs, aux_input["style_feature"], aux_input["d_vectors"])
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if self.num_speakers > 1:
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if not self.use_d_vector_file:
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# B x 1 x speaker_embed_dim
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@ -215,7 +215,7 @@ class Tacotron2(BaseTacotron):
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if self.gst and self.use_gst:
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# B x gst_dim
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encoder_outputs = self.compute_gst(encoder_outputs, aux_input["style_mel"], aux_input["d_vectors"])
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encoder_outputs = self.compute_gst(encoder_outputs, aux_input["style_feature"], aux_input["d_vectors"])
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if self.num_speakers > 1:
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if not self.use_d_vector_file:
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@ -508,6 +508,8 @@ class VitsArgs(Coqpit):
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# prosody encoder
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use_prosody_encoder: bool = False
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prosody_embedding_dim: int = 0
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prosody_encoder_num_heads: int = 1
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prosody_encoder_num_tokens: int = 5
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detach_dp_input: bool = True
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use_language_embedding: bool = False
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@ -642,8 +644,8 @@ class Vits(BaseTTS):
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if self.args.use_prosody_encoder:
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self.prosody_encoder = GST(
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num_mel=self.args.hidden_channels,
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num_heads=1,
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num_style_tokens=5,
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num_heads=self.args.prosody_encoder_num_heads,
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num_style_tokens=self.args.prosody_encoder_num_tokens,
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gst_embedding_dim=self.args.prosody_embedding_dim,
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)
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self.speaker_reversal_classifier = ReversalClassifier(
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@ -840,7 +842,7 @@ class Vits(BaseTTS):
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@staticmethod
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def _set_cond_input(aux_input: Dict):
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"""Set the speaker conditioning input based on the multi-speaker mode."""
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sid, g, lid, eid, eg = None, None, None, None, None
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sid, g, lid, eid, eg, pf = None, None, None, None, None, None
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if "speaker_ids" in aux_input and aux_input["speaker_ids"] is not None:
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sid = aux_input["speaker_ids"]
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if sid.ndim == 0:
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@ -865,7 +867,11 @@ class Vits(BaseTTS):
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if eg.ndim == 2:
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eg = eg.unsqueeze_(0)
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return sid, g, lid, eid, eg
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if "style_feature" in aux_input and aux_input["style_feature"] is not None:
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pf = aux_input["style_feature"]
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if pf.ndim == 2:
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pf = pf.unsqueeze_(0)
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return sid, g, lid, eid, eg, pf
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def _set_speaker_input(self, aux_input: Dict):
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d_vectors = aux_input.get("d_vectors", None)
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@ -968,7 +974,7 @@ class Vits(BaseTTS):
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- syn_cons_emb: :math:`[B, 1, speaker_encoder.proj_dim]`
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"""
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outputs = {}
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sid, g, lid, eid, eg = self._set_cond_input(aux_input)
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sid, g, lid, eid, eg, _ = self._set_cond_input(aux_input)
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# speaker embedding
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if self.args.use_speaker_embedding and sid is not None:
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g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
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@ -998,14 +1004,14 @@ class Vits(BaseTTS):
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if self.args.use_prosody_encoder:
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pros_emb = self.prosody_encoder(z).transpose(1, 2)
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_, l_pros_speaker = self.speaker_reversal_classifier(pros_emb.transpose(1, 2), sid, x_mask=None)
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# print("Encoder input", x.shape)
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x, m_p, logs_p, x_mask = self.text_encoder(x, x_lengths, lang_emb=lang_emb, emo_emb=eg, pros_emb=pros_emb)
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# print("X shape:", x.shape, "m_p shape:", m_p.shape, "x_mask:", x_mask.shape, "x_lengths:", x_lengths.shape)
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# flow layers
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z_p = self.flow(z, y_mask, g=g)
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# print("Y mask:", y_mask.shape)
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# duration predictor
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g_dp = g
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g_dp = g if self.args.condition_dp_on_speaker else None
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if eg is not None and (self.args.use_emotion_embedding or self.args.use_external_emotions_embeddings) and self.args.emotion_just_encoder:
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if g_dp is None:
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g_dp = eg
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@ -1092,6 +1098,7 @@ class Vits(BaseTTS):
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"language_ids": None,
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"emotion_embeddings": None,
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"emotion_ids": None,
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"style_feature": None,
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},
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): # pylint: disable=dangerous-default-value
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"""
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@ -1112,7 +1119,7 @@ class Vits(BaseTTS):
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- m_p: :math:`[B, C, T_dec]`
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- logs_p: :math:`[B, C, T_dec]`
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"""
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sid, g, lid, eid, eg = self._set_cond_input(aux_input)
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sid, g, lid, eid, eg, pf = self._set_cond_input(aux_input)
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x_lengths = self._set_x_lengths(x, aux_input)
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# speaker embedding
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@ -1135,29 +1142,42 @@ class Vits(BaseTTS):
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if self.args.use_language_embedding and lid is not None:
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lang_emb = self.emb_l(lid).unsqueeze(-1)
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x, m_p, logs_p, x_mask = self.text_encoder(x, x_lengths, lang_emb=lang_emb, emo_emb=eg)
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# prosody embedding
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pros_emb = None
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if self.args.use_prosody_encoder:
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# extract posterior encoder feature
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pf_lengths = torch.tensor([pf.size(-1)]).to(pf.device)
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z_pro, _, _, _ = self.posterior_encoder(pf, pf_lengths, g=g)
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pros_emb = self.prosody_encoder(z_pro).transpose(1, 2)
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x, m_p, logs_p, x_mask = self.text_encoder(x, x_lengths, lang_emb=lang_emb, emo_emb=eg, pros_emb=pros_emb)
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# duration predictor
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g_dp = g if self.args.condition_dp_on_speaker else None
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if eg is not None and (self.args.use_emotion_embedding or self.args.use_external_emotions_embeddings) and self.args.emotion_just_encoder:
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if g is None:
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if g_dp is None:
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g_dp = eg
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else:
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g_dp = torch.cat([g, eg], dim=1) # [b, h1+h2, 1]
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else:
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g_dp = g
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g_dp = torch.cat([g_dp, eg], dim=1) # [b, h1+h2, 1]
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if self.args.use_prosody_encoder:
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if g_dp is None:
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g_dp = pros_emb
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else:
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g_dp = torch.cat([g_dp, pros_emb], dim=1) # [b, h1+h2, 1]
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if self.args.use_sdp:
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logw = self.duration_predictor(
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x,
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x_mask,
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g=g_dp if self.args.condition_dp_on_speaker else None,
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g=g_dp,
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reverse=True,
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noise_scale=self.inference_noise_scale_dp,
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lang_emb=lang_emb,
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)
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else:
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logw = self.duration_predictor(
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x, x_mask, g=g_dp if self.args.condition_dp_on_speaker else None, lang_emb=lang_emb
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x, x_mask, g=g_dp, lang_emb=lang_emb
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)
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w = torch.exp(logw) * x_mask * self.length_scale
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@ -1175,9 +1195,22 @@ class Vits(BaseTTS):
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z = self.flow(z_p, y_mask, g=g, reverse=True)
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o = self.waveform_decoder((z * y_mask)[:, :, : self.max_inference_len], g=g)
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outputs = {"model_outputs": o, "alignments": attn.squeeze(1), "z": z, "z_p": z_p, "m_p": m_p, "logs_p": logs_p}
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outputs = {"model_outputs": o, "alignments": attn.squeeze(1), "z": z, "z_p": z_p, "m_p": m_p, "logs_p": logs_p, "durations": w_ceil}
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return outputs
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def compute_style_feature(self, style_wav_path):
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style_wav, sr = torchaudio.load(style_wav_path)
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if sr != self.config.audio.sample_rate:
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raise RuntimeError(" [!] Style reference need to have sampling rate equal to {self.config.audio.sample_rate} !!")
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y = wav_to_spec(
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style_wav,
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self.config.audio.fft_size,
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self.config.audio.hop_length,
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self.config.audio.win_length,
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center=False,
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)
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return y
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@torch.no_grad()
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def inference_voice_conversion(
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self, reference_wav, speaker_id=None, d_vector=None, reference_speaker_id=None, reference_d_vector=None
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@ -14,18 +14,18 @@ def numpy_to_torch(np_array, dtype, cuda=False):
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return tensor
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def compute_style_mel(style_wav, ap, cuda=False):
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style_mel = torch.FloatTensor(ap.melspectrogram(ap.load_wav(style_wav, sr=ap.sample_rate))).unsqueeze(0)
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def compute_style_feature(style_wav, ap, cuda=False):
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style_feature = torch.FloatTensor(ap.melspectrogram(ap.load_wav(style_wav, sr=ap.sample_rate))).unsqueeze(0)
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if cuda:
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return style_mel.cuda()
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return style_mel
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return style_feature.cuda()
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return style_feature
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def run_model_torch(
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model: nn.Module,
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inputs: torch.Tensor,
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speaker_id: int = None,
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style_mel: torch.Tensor = None,
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style_feature: torch.Tensor = None,
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d_vector: torch.Tensor = None,
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language_id: torch.Tensor = None,
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emotion_id: torch.Tensor = None,
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@ -37,7 +37,7 @@ def run_model_torch(
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model (nn.Module): The model to run inference.
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inputs (torch.Tensor): Input tensor with character ids.
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speaker_id (int, optional): Input speaker ids for multi-speaker models. Defaults to None.
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style_mel (torch.Tensor, optional): Spectrograms used for voice styling . Defaults to None.
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style_feature (torch.Tensor, optional): Spectrograms used for voice styling . Defaults to None.
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d_vector (torch.Tensor, optional): d-vector for multi-speaker models . Defaults to None.
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Returns:
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@ -54,7 +54,7 @@ def run_model_torch(
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"x_lengths": input_lengths,
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"speaker_ids": speaker_id,
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"d_vectors": d_vector,
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"style_mel": style_mel,
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"style_feature": style_feature,
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"language_ids": language_id,
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"emotion_ids": emotion_id,
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"emotion_embeddings": emotion_embedding,
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@ -161,12 +161,17 @@ def synthesis(
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Language ID passed to the language embedding layer in multi-langual model. Defaults to None.
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"""
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# GST processing
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style_mel = None
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if CONFIG.has("gst") and CONFIG.gst and style_wav is not None:
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if isinstance(style_wav, dict):
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style_mel = style_wav
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else:
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style_mel = compute_style_mel(style_wav, model.ap, cuda=use_cuda)
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style_feature = None
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if style_wav is not None:
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if CONFIG.has("gst") and CONFIG.gst:
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if isinstance(style_wav, dict):
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style_feature = style_wav
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else:
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style_feature = compute_style_feature(style_wav, model.ap, cuda=use_cuda)
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if hasattr(model, 'compute_style_feature'):
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style_feature = model.compute_style_feature(style_wav)
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# convert text to sequence of token IDs
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text_inputs = np.asarray(
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model.tokenizer.text_to_ids(text, language=language_id),
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@ -188,8 +193,8 @@ def synthesis(
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if emotion_embedding is not None:
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emotion_embedding = embedding_to_torch(emotion_embedding, 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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if not isinstance(style_feature, dict):
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style_feature = numpy_to_torch(style_feature, torch.float, cuda=use_cuda)
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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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# synthesize voice
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@ -197,7 +202,7 @@ def synthesis(
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model,
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text_inputs,
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speaker_id,
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style_mel,
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style_feature,
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d_vector=d_vector,
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language_id=language_id,
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emotion_id=emotion_id,
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@ -47,7 +47,7 @@ config.model_args.use_emotion_embedding = False
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config.model_args.emotion_embedding_dim = 256
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config.model_args.emotion_just_encoder = True
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config.model_args.external_emotions_embs_file = "tests/data/ljspeech/speakers.json"
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config.use_style_weighted_sampler = True
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# consistency loss
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# config.model_args.use_emotion_encoder_as_loss = True
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# config.model_args.encoder_model_path = "/raid/edresson/dev/Checkpoints/Coqui-Realesead/tts_models--multilingual--multi-dataset--your_tts/model_se.pth.tar"
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@ -64,6 +64,13 @@ command_train = (
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"--coqpit.datasets.0.meta_file_val metadata.csv "
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"--coqpit.datasets.0.path tests/data/ljspeech "
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"--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
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"--coqpit.datasets.0.speech_style style1 "
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"--coqpit.datasets.1.name ljspeech_test "
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"--coqpit.datasets.1.meta_file_train metadata.csv "
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"--coqpit.datasets.1.meta_file_val metadata.csv "
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"--coqpit.datasets.1.path tests/data/ljspeech "
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"--coqpit.datasets.1.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
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"--coqpit.datasets.1.speech_style style2 "
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"--coqpit.test_delay_epochs 0"
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)
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run_cli(command_train)
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@ -26,7 +26,7 @@ config = VitsConfig(
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print_step=1,
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print_eval=True,
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test_sentences=[
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["Be a voice, not an echo.", "ljspeech-1", None, None, "ljspeech-1"],
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["Be a voice, not an echo.", "ljspeech-1", "tests/data/ljspeech/wavs/LJ001-0001.wav", None, None],
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],
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)
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# set audio config
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@ -38,11 +38,11 @@ config.model_args.use_speaker_embedding = True
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config.model_args.use_d_vector_file = False
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config.model_args.d_vector_file = "tests/data/ljspeech/speakers.json"
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config.model_args.speaker_embedding_channels = 128
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config.model_args.d_vector_dim = 256
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config.model_args.d_vector_dim = 128
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# prosody embedding
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config.model_args.use_prosody_encoder = True
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config.model_args.prosody_embedding_dim = 256
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config.model_args.prosody_embedding_dim = 64
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config.save_json(config_path)
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@ -67,12 +67,11 @@ continue_config_path = os.path.join(continue_path, "config.json")
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continue_restore_path, _ = get_last_checkpoint(continue_path)
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out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
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speaker_id = "ljspeech-1"
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emotion_id = "ljspeech-3"
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style_wav_path = "tests/data/ljspeech/wavs/LJ001-0001.wav"
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continue_speakers_path = os.path.join(continue_path, "speakers.json")
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continue_emotion_path = os.path.join(continue_path, "speakers.json")
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inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --emotion_idx {emotion_id} --speakers_file_path {continue_speakers_path} --emotions_file_path {continue_emotion_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
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inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path} --gst_style {style_wav_path}"
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run_cli(inference_command)
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# restore the model and continue training for one more epoch
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|
|
|
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Loading…
Reference in New Issue