Update sweep & eval_multiple with new voices

api.py

@@ -140,6 +140,13 @@ class TextToSpeech:
                                       average_conditioning_embeddings=True).cpu().eval()
         self.autoregressive.load_state_dict(torch.load('.models/autoregressive_diverse.pth'))
 
+        self.autoregressive_for_latents = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
+                                      model_dim=1024,
+                                      heads=16, number_text_tokens=256, start_text_token=255, checkpointing=False,
+                                      train_solo_embeddings=False,
+                                      average_conditioning_embeddings=True).cpu().eval()
+        self.autoregressive_for_latents.load_state_dict(torch.load('.models/autoregressive_diverse.pth'))
+
         self.clip = VoiceCLIP(dim_text=512, dim_speech=512, dim_latent=512, num_text_tokens=256, text_enc_depth=12,
                              text_seq_len=350, text_heads=8,
                              num_speech_tokens=8192, speech_enc_depth=12, speech_heads=8, speech_seq_len=430,
@@ -221,11 +228,11 @@ class TextToSpeech:
             # The diffusion model actually wants the last hidden layer from the autoregressive model as conditioning
             # inputs. Re-produce those for the top results. This could be made more efficient by storing all of these
             # results, but will increase memory usage.
-            self.autoregressive = self.autoregressive.cuda()
-            best_latents = self.autoregressive(conds, text, torch.tensor([text.shape[-1]], device=conds.device), best_results,
+            self.autoregressive_for_latents = self.autoregressive_for_latents.cuda()
+            best_latents = self.autoregressive_for_latents(conds, text, torch.tensor([text.shape[-1]], device=conds.device), best_results,
                                                torch.tensor([best_results.shape[-1]*self.autoregressive.mel_length_compression], device=conds.device),
                                                return_latent=True, clip_inputs=False)
-            self.autoregressive = self.autoregressive.cpu()
+            self.autoregressive_for_latents = self.autoregressive_for_latents.cpu()
 
             print("Performing vocoding..")
             wav_candidates = []

eval_multiple.py

@@ -6,32 +6,35 @@ from api import TextToSpeech
 from utils.audio import load_audio
 
 if __name__ == '__main__':
-    fname = 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv'
-    outpath = 'D:\\tmp\\tortoise-tts-eval\\diverse_new_decoder_1'
+    fname = 'Y:\\clips\\books2\\subset512-oco.tsv'
+    stop_after = 128
+    outpath_base = 'D:\\tmp\\tortoise-tts-eval\\diverse'
     outpath_real = 'D:\\tmp\\tortoise-tts-eval\\real'
 
-    os.makedirs(outpath, exist_ok=True)
     os.makedirs(outpath_real, exist_ok=True)
     with open(fname, 'r', encoding='utf-8') as f:
         lines = [l.strip().split('\t') for l in f.readlines()]
 
-    recorder = open(os.path.join(outpath, 'transcript.tsv'), 'w', encoding='utf-8')
     tts = TextToSpeech()
-    for e, line in enumerate(lines):
-        transcript = line[0]
-        if len(transcript) > 120:
-            continue  # We need to support this, but cannot yet.
-        path = os.path.join(os.path.dirname(fname), line[1])
-        cond_audio = load_audio(path, 22050)
-        torchaudio.save(os.path.join(outpath_real, os.path.basename(line[1])), cond_audio, 22050)
-        sample = tts.tts(transcript, [cond_audio, cond_audio], num_autoregressive_samples=256, k=1,
-                             repetition_penalty=2.0, length_penalty=2, temperature=.5, top_p=.5,
-                             diffusion_temperature=.7, cond_free_k=2, diffusion_iterations=100)
+    for k in range(4):
+        outpath = f'{outpath_base}_{k}'
+        os.makedirs(outpath, exist_ok=True)
+        recorder = open(os.path.join(outpath, 'transcript.tsv'), 'w', encoding='utf-8')
+        for e, line in enumerate(lines):
+            if e >= stop_after:
+                break
+            transcript = line[0]
+            path = os.path.join(os.path.dirname(fname), line[1])
+            cond_audio = load_audio(path, 22050)
+            torchaudio.save(os.path.join(outpath_real, os.path.basename(line[1])), cond_audio, 22050)
+            sample = tts.tts(transcript, [cond_audio, cond_audio], num_autoregressive_samples=128, k=1,
+                                 repetition_penalty=2.0, length_penalty=2, temperature=.5, top_p=.5,
+                                 diffusion_temperature=.7, cond_free_k=2, diffusion_iterations=70)
 
-        down = torchaudio.functional.resample(sample, 24000, 22050)
-        fout_path = os.path.join(outpath, os.path.basename(line[1]))
-        torchaudio.save(fout_path, down.squeeze(0), 22050)
+            down = torchaudio.functional.resample(sample, 24000, 22050)
+            fout_path = os.path.join(outpath, os.path.basename(line[1]))
+            torchaudio.save(fout_path, down.squeeze(0), 22050)
 
-        recorder.write(f'{transcript}\t{fout_path}\n')
-        recorder.flush()
-    recorder.close()
+            recorder.write(f'{transcript}\t{fout_path}\n')
+            recorder.flush()
+        recorder.close()

read.py

@@ -30,24 +30,13 @@ if __name__ == '__main__':
     # These are voices drawn randomly from the training set. You are free to substitute your own voices in, but testing
     # has shown that the model does not generalize to new voices very well.
     preselected_cond_voices = {
-        # Male voices
-        'dotrice': ['voices/dotrice/1.wav', 'voices/dotrice/2.wav'],
-        'harris': ['voices/harris/1.wav', 'voices/harris/2.wav'],
-        'lescault': ['voices/lescault/1.wav', 'voices/lescault/2.wav'],
-        'otto': ['voices/otto/1.wav', 'voices/otto/2.wav'],
-        'obama': ['voices/obama/1.wav', 'voices/obama/2.wav'],
-        'carlin': ['voices/carlin/1.wav', 'voices/carlin/2.wav'],
-        # Female voices
-        'atkins': ['voices/atkins/1.wav', 'voices/atkins/2.wav'],
-        'grace': ['voices/grace/1.wav', 'voices/grace/2.wav'],
-        'kennard': ['voices/kennard/1.wav', 'voices/kennard/2.wav'],
-        'mol': ['voices/mol/1.wav', 'voices/mol/2.wav'],
-        'lj': ['voices/lj/1.wav', 'voices/lj/2.wav'],
+        'emma_stone': ['voices/emma_stone/1.wav','voices/emma_stone/2.wav','voices/emma_stone/3.wav'],
+        'tom_hanks': ['voices/tom_hanks/1.wav','voices/tom_hanks/2.wav','voices/tom_hanks/3.wav'],
     }
 
     parser = argparse.ArgumentParser()
     parser.add_argument('-textfile', type=str, help='A file containing the text to read.', default="data/riding_hood.txt")
-    parser.add_argument('-voice', type=str, help='Use a preset conditioning voice (defined above). Overrides cond_path.', default='dotrice')
+    parser.add_argument('-voice', type=str, help='Use a preset conditioning voice (defined above). Overrides cond_path.', default='emma_stone')
     parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=512)
     parser.add_argument('-batch_size', type=int, help='How many samples to process at once in the autoregressive model.', default=16)
     parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='results/longform/')