sanity checks (and I realized that the model actually had langs set to 4 in the yaml for KO/ZH so................

scripts/process_nscripter.py

@@ -0,0 +1,52 @@
+"""
+Handles processing NScripter's 0.u file to clean up the pile of audio clips it has
+
+* to-do: also grab transcriptions
+"""
+
+import os
+import re
+import json
+import argparse
+import torch
+import shutil
+import torchaudio
+import numpy as np
+
+from tqdm.auto import tqdm
+from pathlib import Path
+
+def process(
+	input_file=Path("./assets/0.u"),
+	wav_dir=Path("./arc/"),
+	output_dir=Path("./dataset/"),
+):
+	file = open(input_file, encoding='utf-8').read()
+
+	names = {}
+	aliases = {}
+	lines = file.split('\n')
+
+	for line in lines:
+		if not line.startswith('stralias'):
+			continue
+		# ick
+		try:
+			key, path = re.findall(r'^stralias (.+?),"(.+?)"$', line)[0]
+			name = key.split("_")[0]
+			if name not in names:
+				(output_dir / name).mkdir(parents=True, exist_ok=True)
+				names[name] = True
+
+			aliases[key] = Path(path)
+		except Exception as e:
+			pass
+
+	for k, v in aliases.items():
+		name = k.split("_")[0]
+
+
+	print(aliases)
+
+if __name__ == "__main__":
+	process()

scripts/process_seed-tts.py

@@ -18,10 +18,10 @@ from tqdm.auto import tqdm
 from pathlib import Path
 
 def process(
-	input_dir=Path("./seedtts_testset/en/"),
-	list_name="./meta.lst",
+	input_dir=Path("./seedtts_testset/zh/"),
+	list_name="./hardcase.lst",
 	wav_dir="./wavs/",
-	output_dir=Path("./dataset/seed-tts-eval-en/"),
+	output_dir=Path("./dataset/seed-tts-eval-hard/"),
 ):
 	language = "auto"
 	
@@ -46,7 +46,11 @@ def process(
 		open( output_dir / filename / "prompt.txt", "w", encoding="utf-8" ).write( text )
 		open( output_dir / filename / "language.txt", "w", encoding="utf-8" ).write( language )
 
-		shutil.copy((input_dir / wav_dir / filename).with_suffix(".wav"), output_dir / filename / "reference.wav" )
+		reference_wav = (input_dir / wav_dir / filename).with_suffix(".wav")
+		if not reference_wav.exists():
+			continue
+
+		shutil.copy(reference_wav, output_dir / filename / "reference.wav" )
 		shutil.copy(input_dir / prompt_wav, output_dir / filename / "prompt.wav" )
 
 if __name__ == "__main__":

vall_e/data.py

@@ -192,7 +192,7 @@ def normalize_text(text, language="auto", full=True):
 	return text
 
 @cache
-def get_random_prompts( validation=False, min_length=0, tokenized=False, source_path=Path("./data/harvard_sentences.txt") ):
+def get_random_prompts( validation=False, min_length=0, tokenized=False, source_path=Path("./data/tongue_twisters.txt") ):
 	duration_range = [ 5.5, 12.0 ] # to-do: pull from cfg.dataset.duration_range
 	sentences = [
 		"The birch canoe slid on the smooth planks.",

vall_e/demo.py

@@ -289,10 +289,10 @@ def main():
 
 	# only add the existing librispeech validation dataset if i'm doing validation so I can stop commenting this out
 	if not args.dataset_dir_name:
-		samples_dirs["librispeech"] = args.demo_dir / "librispeech"
+		samples_dirs["librispeech"] = args.demo_dir / "datasets" / "librispeech"
 	else:
 		if "validation" in args.dataset_dir_name:
-			samples_dirs["librispeech"] = args.demo_dir / "librispeech"
+			samples_dirs["librispeech"] = args.demo_dir / "datasets" / "librispeech"
 
 		# automatically pull from anything under the dataset dir
 		if args.dataset_dir_name.endswith("/*"):
@@ -444,7 +444,7 @@ def main():
 		if dataset_name not in metrics_map:
 			metrics_map[dataset_name] = {}
 
-		metrics_map[dataset_name][out_path] = (wer_score, cer_score, sim_o_score)
+		metrics_map[dataset_name][out_path] = (wer_score, cer_score, per_score, sim_o_score)
 
 	# collate entries into HTML
 	tables = []

vall_e/export.py

@@ -13,15 +13,13 @@ from .utils.io import torch_save, torch_load
 # *will* require retraining because the classifier is in one contiguous space, and proms are NOT summed
 @torch.no_grad()
 def convert_to_hf( state_dict, config = None, save_path = None ):
-	# to-do: infer all of this from the existing state_dict, should be easy by checking shape
-	model_dim = 1024
+	n_text_tokens, model_dim = state_dict['module']['text_emb.weight'].shape
 
-	n_text_tokens = 256
-	n_audio_tokens = 1024
-	n_resp_levels = 8
+	n_audio_tokens = state_dict['module']['proms_emb.embeddings.0.weight'].shape[0]
+	n_resp_levels = state_dict['module']['rvq_l_emb.weight'].shape[0]
 	n_len_tokens = 11
-	n_lang_tokens = 4
-	n_task_tokens = 9
+	n_lang_tokens = state_dict['module']['langs_emb.weight'].shape[0]
+	n_task_tokens = state_dict['module']['tasks_emb.weight'].shape[0]
 
 	# the new tokenizer to use
 	tokenizer_append = {}
@@ -45,6 +43,8 @@ def convert_to_hf( state_dict, config = None, save_path = None ):
 		"ja",
 		"de",
 		"fr",
+		"zh",
+		"ko",
 	]
 	task_map = [
 		"tts",
@@ -100,8 +100,8 @@ def convert_to_hf( state_dict, config = None, save_path = None ):
 	token_start = token_end
 	token_end += l_tokens[2] // 2
 	embedding.weight[token_start:token_end] = state_dict['module'][f'resps_emb.embeddings.8.weight']
-	classifier.weight[token_start:token_end] = state_dict['module']['classifiers.proj.8.weight']
-	classifier.bias[token_start:token_end] = state_dict['module']['classifiers.proj.8.bias']
+	classifier.weight[token_start:token_end-1] = state_dict['module']['classifiers.proj.8.weight']
+	classifier.bias[token_start:token_end-1] = state_dict['module']['classifiers.proj.8.bias']
 	for t in range(n_audio_tokens):
 		tokenizer_append[f'<NAR:0:0:{t}>'] = token_start + t
 	tokenizer_append[f'<NAR:0:0:STOP>'] = token_start + 1024