Fix voice conversion inference (#1583)

* Add voice conversion zoo test

* Fix style

* Fix unit test

TTS/bin/synthesize.py

@@ -171,7 +171,7 @@ If you don't specify any models, then it uses LJSpeech based English model.
         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.",
         default=None,
     )
-    parser.add_argument("--gst_style", help="Wav path file for GST stylereference.", default=None)
+    parser.add_argument("--gst_style", help="Wav path file for GST style reference.", default=None)
     parser.add_argument(
         "--list_speaker_idxs",
         help="List available speaker ids for the defined multi-speaker model.",

TTS/tts/models/vits.py

@@ -1127,7 +1127,7 @@ class Vits(BaseTTS):
             self.config.audio.hop_length,
             self.config.audio.win_length,
             center=False,
-        ).transpose(1, 2)
+        )
         y_lengths = torch.tensor([y.size(-1)]).to(y.device)
         speaker_cond_src = reference_speaker_id if reference_speaker_id is not None else reference_d_vector
         speaker_cond_tgt = speaker_id if speaker_id is not None else d_vector
@@ -1157,7 +1157,7 @@ class Vits(BaseTTS):
         else:
             raise RuntimeError(" [!] Voice conversion is only supported on multi-speaker models.")
 
-        z, _, _, y_mask = self.posterior_encoder(y.transpose(1, 2), y_lengths, g=g_src)
+        z, _, _, y_mask = self.posterior_encoder(y, y_lengths, g=g_src)
         z_p = self.flow(z, y_mask, g=g_src)
         z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
         o_hat = self.waveform_decoder(z_hat * y_mask, g=g_tgt)

TTS/utils/synthesizer.py

@@ -315,7 +315,7 @@ class Synthesizer(object):
             # get the speaker embedding or speaker id for the reference wav file
             reference_speaker_embedding = None
             reference_speaker_id = None
-            if self.tts_speakers_file or hasattr(self.tts_model.speaker_manager, "speaker_ids"):
+            if self.tts_speakers_file or hasattr(self.tts_model.speaker_manager, "ids"):
                 if reference_speaker_name and isinstance(reference_speaker_name, str):
                     if self.tts_config.use_d_vector_file:
                         # get the speaker embedding from the saved d_vectors.

tests/tts_tests/test_vits.py

@@ -122,7 +122,7 @@ class TestVits(unittest.TestCase):
         args = VitsArgs(num_speakers=num_speakers, use_speaker_embedding=True)
         model = Vits(args)
 
-        ref_inp = torch.randn(1, spec_len, 513)
+        ref_inp = torch.randn(1, 513, spec_len)
         ref_inp_len = torch.randint(1, spec_effective_len, (1,))
         ref_spk_id = torch.randint(1, num_speakers, (1,))
         tgt_spk_id = torch.randint(1, num_speakers, (1,))

tests/zoo_tests/test_models.py

@@ -3,7 +3,7 @@ import glob
 import os
 import shutil
 
-from tests import get_tests_output_path, run_cli
+from tests import get_tests_data_path, get_tests_output_path, run_cli
 from TTS.tts.utils.languages import LanguageManager
 from TTS.tts.utils.speakers import SpeakerManager
 from TTS.utils.generic_utils import get_user_data_dir
@@ -56,3 +56,16 @@ def test_run_all_models():
     folders = glob.glob(os.path.join(manager.output_prefix, "*"))
     assert len(folders) == len(model_names)
     shutil.rmtree(manager.output_prefix)
+
+
+def test_voice_conversion():
+    print(" > Run voice conversion inference using YourTTS model.")
+    model_name = "tts_models/multilingual/multi-dataset/your_tts"
+    language_id = "en"
+    speaker_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav")
+    reference_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0032.wav")
+    output_path = os.path.join(get_tests_output_path(), "output.wav")
+    run_cli(
+        f"tts --model_name  {model_name}"
+        f" --out_path {output_path} --speaker_wav {speaker_wav} --reference_wav {reference_wav} --language_idx {language_id} "
+    )