oops, turns out these are not split by speaker names already........ (also added sampling the dataset in the webui for easy viewing)

scripts/process_emilia.py

@@ -100,29 +100,29 @@ def process(
 
 		group_name = "Emilia"
 
-		for speaker_id in tqdm(process_items(os.listdir(f'./{input_audio}/{language}/'), stride=stride, stride_offset=stride_offset), desc=f"Processing speaker in {language}"):
-			if not os.path.isdir(f'./{input_audio}/{language}/{speaker_id}'):
-				print("Is not dir:", f'./{input_audio}/{language}/{speaker_id}')
+		for speaker_group in tqdm(process_items(os.listdir(f'./{input_audio}/{language}/'), stride=stride, stride_offset=stride_offset), desc=f"Processing speaker in {language}"):
+			if not os.path.isdir(f'./{input_audio}/{language}/{speaker_group}'):
+				print("Is not dir:", f'./{input_audio}/{language}/{speaker_group}')
 				continue
 			
-			if speaker_id in ignore_speakers:
+			if speaker_group in ignore_speakers:
 				continue
-			if only_speakers and speaker_id not in only_speakers:
+			if only_speakers and speaker_group not in only_speakers:
 				continue
 
-			os.makedirs(f'./{output_dataset}/{group_name}/{speaker_id}/', exist_ok=True)
+			os.makedirs(f'./{output_dataset}/{group_name}/{speaker_group}/', exist_ok=True)
 
-			if f'{group_name}/{speaker_id}' not in dataset:
-				dataset.append(f'{group_name}/{speaker_id}')
+			if f'{group_name}/{speaker_group}' not in dataset:
+				dataset.append(f'{group_name}/{speaker_group}')
 
 			txts = []
 			wavs = []
 
-			for filename in os.listdir(f'./{input_audio}/{language}/{speaker_id}'):
+			for filename in os.listdir(f'./{input_audio}/{language}/{speaker_group}'):
 				if ".mp3" not in filename:
 					continue
 
-				inpath = Path(f'./{input_audio}/{language}/{speaker_id}/{filename}')
+				inpath = Path(f'./{input_audio}/{language}/{speaker_group}/{filename}')
 				jsonpath = _replace_file_extension(inpath, ".json")
 				if not inpath.exists() or not jsonpath.exists():
 					missing["audio"].append(str(inpath))
@@ -130,15 +130,15 @@ def process(
 			
 				extension = os.path.splitext(filename)[-1][1:]
 				fname = filename.replace(f'.{extension}', "")
-
-				waveform, sample_rate = None, None
-
-				outpath = Path(f'./{output_dataset}/{group_name}/{speaker_id}/{fname}.{extension}')
-				metadata = json.load(open(jsonpath, "r", encoding="utf-8"))
-
 				if "text" not in metadata:
 					continue
 
+				waveform, sample_rate = None, None
+				metadata = json.load(open(jsonpath, "r", encoding="utf-8"))
+				speaker_id = metadata["speaker"]
+				outpath = Path(f'./{output_dataset}/{group_name}/{speaker_id}/{fname}.{extension}')
+
+
 				if _replace_file_extension(outpath, audio_extension).exists():
 					continue
 

vall_e/utils/io.py

@@ -13,9 +13,13 @@ except:
 
 from .utils import truncate_json
 
-def json_stringify( data, truncate=False ):
+def json_stringify( data, truncate=False, pretty=False ):
 	if truncate:
 		return truncate_json( json.dumps( data ) )
+	if pretty:
+		if use_orjson:
+			return json.dumps( data, option=json.OPT_INDENT_2 ).decode('utf-8')
+		return json.dumps( data, indent='\t' ).decode('utf-8')
 	return json.dumps( data )
 
 def json_parse( string ):

vall_e/webui.py

@@ -5,6 +5,10 @@ import argparse
 import random
 import tempfile
 import functools
+
+import torch
+import numpy as np
+
 from datetime import datetime
 
 import torchaudio
@@ -16,6 +20,8 @@ from pathlib import Path
 from .inference import TTS, cfg
 from .train import train
 from .utils import get_devices, setup_logging
+from .utils.io import json_read, json_stringify
+from .emb.qnt import decode_to_wave
 
 tts = None
 
@@ -23,6 +29,7 @@ layout = {}
 layout["inference_tts"] = {}
 layout["inference_stt"] = {}
 layout["training"] = {}
+layout["dataset"] = {}
 layout["settings"] = {}
 
 for k in layout.keys():
@@ -90,6 +97,29 @@ def load_model( yaml, device, dtype, attention ):
 		raise gr.Error(e)
 	gr.Info(f"Loaded model")
 
+def get_speakers():
+	return cfg.dataset.training
+
+#@gradio_wrapper(inputs=layout["dataset"]["inputs"].keys())
+def load_sample( speaker ):
+	metadata_path = cfg.metadata_dir / f'{speaker}.json'
+	metadata = json_read( metadata_path )
+	if not metadata:
+		raise gr.Error(f"Metadata not found: {metadata_path}")
+
+	key = random.choice( list(metadata.keys()) )
+	path = cfg.data_dir / speaker / f'{key}.enc' # to-do: get proper file extension
+	data = json_stringify( metadata[key], pretty=True )
+	wav, sr = None, None
+
+	if path.exists():
+		artifact = np.load(path, allow_pickle=True)[()]
+		codes = torch.from_numpy(artifact["codes"].astype(int))[0].t().to(dtype=torch.int16, device=cfg.device)
+		wav, sr = decode_to_wave( codes )
+		wav = wav.squeeze(0).cpu().numpy()
+
+	return data, (sr, wav)
+
 def init_tts(yaml=None, restart=False, device="cuda", dtype="auto", attention="auto"):
 	global tts
 
@@ -429,6 +459,21 @@ with ui:
 		)
 	"""
 
+	with gr.Tab("Dataset"):
+		with gr.Row():
+			with gr.Column(scale=7):
+				layout["dataset"]["outputs"]["transcription"] = gr.Textbox(lines=5, label="Sample Metadata")
+			with gr.Column(scale=1):
+				layout["dataset"]["inputs"]["speaker"] = gr.Dropdown(choices=get_speakers(), label="Speakers")
+				layout["dataset"]["outputs"]["audio"] = gr.Audio(label="Output")
+				layout["dataset"]["buttons"]["sample"] = gr.Button(value="Sample")
+
+			layout["dataset"]["buttons"]["sample"].click(
+				fn=load_sample,
+				inputs=[ x for x in layout["dataset"]["inputs"].values() if x is not None],
+				outputs=[ x for x in layout["dataset"]["outputs"].values() if x is not None],
+			)
+
 	with gr.Tab("Settings"):
 		with gr.Row():
 			with gr.Column(scale=7):