added min-x and min-y arguments to plot.py, helper script to download from my existing checkpoint

scripts/setup.sh

@@ -0,0 +1,9 @@
+#!/bin/bash
+
+python3 -m venv venv
+pip3 install -e .
+
+mkdir -p ./training/valle/ckpt/ar+nar-retnet-8/
+wget -P ./training/valle/ckpt/ar+nar-retnet-8/fp32.pth "https://huggingface.co/ecker/vall-e/resolve/main/ckpt/ar%2Bnar-retnet-8/fp32.pth"
+wget -P ./training/valle/data.h5 "https://huggingface.co/ecker/vall-e/resolve/main/data.h5"
+wget -P ./training/valle/config.yaml "https://huggingface.co/ecker/vall-e/raw/main/config.yaml"

scripts/stitch_embs.py

@@ -1,28 +0,0 @@
-import torch
-
-action = None
-# copies the resp_embs from a given AR and NAR into an AR as a base to convert into an AR+NAR monolithic odel
-if action == "merge_resp_embs":
-	src_ar = torch.load("./data/source-ar.pth", map_location="cpu")
-	src_nar = torch.load("./data/source-nar.pth", map_location="cpu")
-	# copies all weights from the AR since the AR is usually "better", might need to experiment more with using a NAR as the base
-	dst = torch.load("./data/source-ar.pth", map_location="cpu")
-
-	# copy resps_emb to layer 0 from AR
-	dst['module']['resps_emb.weight'][:0, :, :] = src_ar['module']['resps_emb.weight']
-	# copy resps_emb to remaining layers from NAR
-	dst['module']['resps_emb.weight'][1:, :-1, :] = src_nar['module']['resps_emb.weight']
-# copies an existing AR+NAR monolithic model's resp_emb onto an AR
-elif action == "copy_resps_emb":
-	src = torch.load("./data/source.pth", map_location="cpu")
-	dst = torch.load("./data/destination.pth", map_location="cpu")
-	dst['module']['resps_emb.weight'] = src['module']['resps_emb.weight']
-elif action == "extend_resps_emb":
-	dst = torch.load("./data/destination.pth", map_location="cpu")
-	dst['module']['resps_emb.weight'] = dst['module']['resps_emb.weight'].expand(4, -1, -1)
-	dst['module']['resps_emb.weight'][1:] = torch.randn(3, 1025, 1024)
-
-else
-	raise Exception(f"invalid action: {action}")
-
-torch.save(dst, './data/fp32.pth')

vall_e/config.py

@@ -121,7 +121,7 @@ class Dataset:
 
 	speaker_name_getter: str = "lambda p: f'{p.parts[-3]}_{p.parts[-2]}'"
 	
-	hdf5_name: str = "data.h5"
+	hdf5_name: str = "dataset.h5"
 	use_hdf5: bool = False
 	use_metadata: bool = False
 	hdf5_flag: str = "a"

vall_e/plot.py

@@ -47,7 +47,11 @@ def plot(paths, args):
 
 	df = pd.concat(dfs)
 
-	if args.max_y is not None:
+	if args.min_x is not None:
+		for model in args.models:
+			df = df[args.min_x < df[f'{model.name}.{args.xs}']]
+
+	if args.max_x is not None:
 		for model in args.models:
 			df = df[df[f'{model.name}.{args.xs}'] < args.max_x]
 
@@ -63,6 +67,8 @@ def plot(paths, args):
 				if gdf[y].isna().all():
 					continue
 
+				if args.min_y is not None:
+					gdf = gdf[args.min_y < gdf[y]]
 				if args.max_y is not None:
 					gdf = gdf[gdf[y] < args.max_y]
 
@@ -91,6 +97,8 @@ if __name__ == "__main__":
 	parser.add_argument("--ys", nargs="+", default="")
 	parser.add_argument("--model", nargs="+", default="*")
 
+	parser.add_argument("--min-x", type=float, default=-float("inf"))
+	parser.add_argument("--min-y", type=float, default=-float("inf"))
 	parser.add_argument("--max-x", type=float, default=float("inf"))
 	parser.add_argument("--max-y", type=float, default=float("inf"))