[TTS] Add tutorials for FastPitch TTS speaker adaptation with adapters

examples/tts/conf/fastpitch_align_44100_adapter.yaml

@@ -208,7 +208,7 @@ model:
     dropatt: 0.1
     dropemb: 0.0
     d_embed: ${model.symbols_embedding_dim}
-    condition_types: [ "add", "layernorm" ] # options: [ "add", "cat", "layernorm" ]
+    condition_types: [ "add", "layernorm" ] # options: [ "add", "concat", "layernorm" ]
 
   output_fft:
     _target_: nemo.collections.tts.modules.transformer.FFTransformerDecoder
@@ -221,12 +221,12 @@ model:
     dropout: 0.1
     dropatt: 0.1
     dropemb: 0.0
-    condition_types: [ "add", "layernorm" ] # options: [ "add", "cat", "layernorm" ]
+    condition_types: [ "add", "layernorm" ] # options: [ "add", "concat", "layernorm" ]
 
   alignment_module:
     _target_: nemo.collections.tts.modules.aligner.AlignmentEncoder
     n_text_channels: ${model.symbols_embedding_dim}
-    condition_types: [ "add" ] # options: [ "add", "cat" ]
+    condition_types: [ "add" ] # options: [ "add", "concat" ]
 
   duration_predictor:
     _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor
@@ -235,7 +235,7 @@ model:
     filter_size: 256
     dropout: 0.1
     n_layers: 2
-    condition_types: [ "add", "layernorm" ] # options: [ "add", "cat", "layernorm" ]
+    condition_types: [ "add", "layernorm" ] # options: [ "add", "concat", "layernorm" ]
 
   pitch_predictor:
     _target_: nemo.collections.tts.modules.fastpitch.TemporalPredictor
@@ -244,10 +244,12 @@ model:
     filter_size: 256
     dropout: 0.1
     n_layers: 2
-    condition_types: [ "add", "layernorm" ] # options: [ "add", "cat", "layernorm" ]
+    condition_types: [ "add", "layernorm" ] # options: [ "add", "concat", "layernorm" ]
 
   speaker_encoder:
     _target_: nemo.collections.tts.modules.submodules.SpeakerEncoder
+    precompute: false
+    precompute_embedding_dim: ${model.symbols_embedding_dim}
     lookup_module:
       _target_: nemo.collections.tts.modules.submodules.SpeakerLookupTable
       n_speakers: ???

nemo/collections/tts/models/fastpitch.py

@@ -183,6 +183,9 @@ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
         if self.fastpitch.speaker_emb is not None:
             self.export_config["num_speakers"] = cfg.n_speakers
 
+        # Adapter modules setup (from FastPitchAdapterModelMixin)
+        self.setup_adapters()
+
     def _get_default_text_tokenizer_conf(self):
         text_tokenizer: TextTokenizerConfig = TextTokenizerConfig()
         return OmegaConf.create(OmegaConf.to_yaml(text_tokenizer))

nemo/collections/tts/modules/fastpitch.py

@@ -177,7 +177,6 @@ def __init__(
         self.learn_alignment = aligner is not None
         self.use_duration_predictor = True
         self.binarize = False
-
         # TODO: combine self.speaker_emb with self.speaker_encoder
         # cfg: remove `n_speakers`, create `speaker_encoder.lookup_module`
         # state_dict: move `speaker_emb.weight` to `speaker_encoder.lookup_module.table.weight`
@@ -244,10 +243,10 @@ def output_types(self):
             "energy_tgt": NeuralType(('B', 'T_audio'), RegressionValuesType()),
         }
 
-    def get_speaker_embedding(self, speaker, reference_spec, reference_spec_lens):
+    def get_speaker_embedding(self, batch_size, speaker, reference_spec, reference_spec_lens):
         """spk_emb: Bx1xD"""
         if self.speaker_encoder is not None:
-            spk_emb = self.speaker_encoder(speaker, reference_spec, reference_spec_lens).unsqueeze(1)
+            spk_emb = self.speaker_encoder(batch_size, speaker, reference_spec, reference_spec_lens).unsqueeze(1)
         elif self.speaker_emb is not None:
             if speaker is None:
                 raise ValueError('Please give speaker id to get lookup speaker embedding.')
@@ -281,7 +280,10 @@ def forward(
 
         # Calculate speaker embedding
         spk_emb = self.get_speaker_embedding(
-            speaker=speaker, reference_spec=reference_spec, reference_spec_lens=reference_spec_lens,
+            batch_size=text.shape[0],
+            speaker=speaker,
+            reference_spec=reference_spec,
+            reference_spec_lens=reference_spec_lens,
         )
 
         # Input FFT
@@ -379,10 +381,12 @@ def infer(
         reference_spec=None,
         reference_spec_lens=None,
     ):
-
         # Calculate speaker embedding
         spk_emb = self.get_speaker_embedding(
-            speaker=speaker, reference_spec=reference_spec, reference_spec_lens=reference_spec_lens,
+            batch_size=text.shape[0],
+            speaker=speaker,
+            reference_spec=reference_spec,
+            reference_spec_lens=reference_spec_lens,
         )
 
         # Input FFT

nemo/collections/tts/modules/submodules.py

@@ -709,18 +709,29 @@ class SpeakerEncoder represents speakers representation.
     This module can combine GST (global style token) based speaker embeddings and lookup table speaker embeddings.
     """
 
-    def __init__(self, lookup_module=None, gst_module=None):
+    def __init__(self, lookup_module=None, gst_module=None, precompute=False, precompute_embedding_dim=None):
         """
         lookup_module: Torch module to get lookup based speaker embedding
         gst_module: Neural module to get GST based speaker embedding
+        precompute: Use precompute speaker embedding
         """
         super(SpeakerEncoder, self).__init__()
+
+        # Multi-speaker embedding
         self.lookup_module = lookup_module
+
+        # Reference speaker embedding
         self.gst_module = gst_module
 
+        if precompute:
+            self.precomputed_emb = torch.nn.Parameter(torch.empty(precompute_embedding_dim))
+        else:
+            self.register_parameter('precomputed_emb', None)
+
     @property
     def input_types(self):
         return {
+            "batch_size": NeuralType(),
             "speaker": NeuralType(('B'), Index(), optional=True),
             "reference_spec": NeuralType(('B', 'D', 'T_spec'), MelSpectrogramType(), optional=True),
             "reference_spec_lens": NeuralType(('B'), LengthsType(), optional=True),
@@ -732,9 +743,13 @@ def output_types(self):
             "embs": NeuralType(('B', 'D'), EncodedRepresentation()),
         }
 
-    def forward(self, speaker=None, reference_spec=None, reference_spec_lens=None):
+    def forward(self, batch_size, speaker=None, reference_spec=None, reference_spec_lens=None):
         embs = None
 
+        # Get Precomputed speaker embedding
+        if self.precomputed_emb is not None:
+            return self.precomputed_emb.unsqueeze(0).repeat(batch_size, 1)
+
         # Get Lookup table speaker embedding
         if self.lookup_module is not None and speaker is not None:
             embs = self.lookup_module(speaker)

scripts/dataset_processing/tts/extract_sup_data.py

@@ -31,7 +31,7 @@ def get_pitch_stats(pitch_list):
 def preprocess_ds_for_fastpitch_align(dataloader):
     pitch_list = []
     for batch in tqdm(dataloader, total=len(dataloader)):
-        audios, audio_lengths, tokens, tokens_lengths, align_prior_matrices, pitches, pitches_lengths = batch
+        audios, audio_lengths, tokens, tokens_lengths, align_prior_matrices, pitches, pitches_lengths, *_ = batch
         pitch = pitches.squeeze(0)
         pitch_list.append(pitch[pitch != 0])
 

scripts/dataset_processing/tts/resynthesize_dataset.py

@@ -117,6 +117,15 @@ def resynthesize_batch(self, batch: Dict[str, Any]) -> Dict[str, Any]:
         batch = to_device_recursive(batch, self.device)
 
         mels, mel_lens = self.model.preprocessor(input_signal=batch["audio"], length=batch["audio_lens"])
+
+        reference_audio = batch.get("reference_audio", None)
+        reference_audio_len = batch.get("reference_audio_lens", None)
+        reference_spec, reference_spec_len = None, None
+        if reference_audio is not None:
+            reference_spec, reference_spec_len = self.model.preprocessor(
+                input_signal=reference_audio, length=reference_audio_len
+            )
+
         outputs_tuple = self.model.forward(
             text=batch["text"],
             durs=None,
@@ -127,6 +136,8 @@ def resynthesize_batch(self, batch: Dict[str, Any]) -> Dict[str, Any]:
             attn_prior=batch.get("attn_prior"),
             mel_lens=mel_lens,
             input_lens=batch["text_lens"],
+            reference_spec=reference_spec,
+            reference_spec_lens=reference_spec_len,
         )
         names = self.model.fastpitch.output_types.keys()
         return {"spec": mels, "mel_lens": mel_lens, **dict(zip(names, outputs_tuple))}