culled artifacts left over from the valle trainer
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README.md
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README.md
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This is a simple ResNet based image classifier for """specific images""", using a similar training framework I use to train [VALL-E](https://git.ecker.tech/mrq/vall-e/).
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## Premise
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This was cobbled together in a night, partly to test how well my training framework fares when not married to my VALL-E implementation, and partly to solve a problem I have recently faced. Since I've been balls deep in learning the ins and outs of making VALL-E work, why not do the exact opposite (a tiny, image classification model of fixed lengths) to test the framework and my knowledge? Thus, this """ambiguous""" project is born.
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This is by no ways state of the art, as it just leverages an existing ResNet arch provided by `torchvision`.
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## Training
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1. Throw the images you want to train under `./data/images/`.
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@ -18,8 +24,32 @@ This is a simple ResNet based image classifier for """specific images""", using
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Simply invoke the inferencer with the following command: `python3 -m image_classifier "./data/path-to-your-image.png" yaml="./data/config.yaml" --temp=1.0`
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## Caveats
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## Known Issues
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This was cobbled together in a night, partly to test how well my training framework fares when not married to my VALL-E implementation, and partly to solve a problem I have recently faced. Since I've been balls deep in learning the ins and outs of making VALL-E work, why not do the exact opposite (a tiny, image classification model of fixed lengths) to test the framework and my knowledge? Thus, this """ambiguous""" project is born.
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* Setting `dataset.workers` higher than 0 will cause issues when using the local engine backend. Use DeepSpeed.
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* The evaluation / validation routine doesn't quite work.
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* Using `float16` with the local engine backend will cause instability in the losses. Use DeepSpeed.
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This is by no ways state of the art, as it just leverages an existing ResNet arch provided by `torchvision`.
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## Strawmen
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>\> UGH... Why *another* training framework!!! Just subjugate [DLAS](https://git.ecker.tech/mrq/DL-Art-School) even more!!!
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I want my own code to own. The original VALL-E implementation had a rather nice and clean setup that *mostly* just made sense. DLAS was a nightmare to comb through for the gorillion amounts of models it attests.
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>\> OK. But how do I use it for `[thing that isn't the specific usecase only I know/care about]`
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Simply provide your own symmapping under `./image_classifier/data.py`, and, be sure to set the delimiter (where exactly is an exercise left to the reader).
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Because this is for a ***very specific*** use-case. I don't really care right now to make this a *little* more generalized, despite most of the bits and bobs for it to generalize being there.
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>\> ur `[a slur]` for using a ResNet... why not use `[CRNN / some other meme arch]`??
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I don't care, I'd rather keep the copypasting from other people's code to a minimum. Lazily adapting my phoneme tokenizer from my VALL-E implementation into something practically fixed length by introducing start/stop tokens should be grounds for me to use a CRNN, or anything recurrent at the very least, but again, I don't care, it just works for my use case at the moment.
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>\> UGH!!! What are you talking about """specific images"""???
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[ひみつ](https://files.catbox.moe/csuh49.webm)
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>\> NOOOO!!!! WHY AREN'T YOU USING `[cuck license]`???
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:)
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@ -2,7 +2,6 @@
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from .config import cfg
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from .data import create_train_val_dataloader
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from .emb import qnt
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from .utils import setup_logging, to_device, trainer, flatten_dict, do_gc
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from .utils.trainer import load_engines
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#!/bin/bash
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# do not invoke directly in scripts
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if [[ ${PWD##*/} == 'scripts' ]]; then
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cd ..
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fi
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# download training data
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git clone https://huggingface.co/datasets/ecker/libritts-small ./data/libritts-small
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106
scripts/plot.py
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scripts/plot.py
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#!/usr/bin/env python3
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import argparse
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import json
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import re
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from pathlib import Path
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import matplotlib.pyplot as plt
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import pandas as pd
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def plot(paths, args):
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dfs = []
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for path in paths:
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with open(path, "r") as f:
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text = f.read()
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rows = []
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pattern = r"(\{.+?\})"
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for row in re.findall(pattern, text, re.DOTALL):
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try:
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row = json.loads(row)
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except Exception as e:
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continue
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if "global_step" in row:
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rows.append(row)
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df = pd.DataFrame(rows)
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if "name" in df:
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df["name"] = df["name"].fillna("train")
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else:
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df["name"] = "train"
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df["group"] = str(path.parents[args.group_level])
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df["group"] = df["group"] + "/" + df["name"]
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dfs.append(df)
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df = pd.concat(dfs)
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if args.max_y is not None:
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df = df[df["global_step"] < args.max_x]
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for gtag, gdf in sorted(
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df.groupby("group"),
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key=lambda p: (p[0].split("/")[-1], p[0]),
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):
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for y in args.ys:
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gdf = gdf.sort_values("global_step")
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if gdf[y].isna().all():
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continue
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if args.max_y is not None:
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gdf = gdf[gdf[y] < args.max_y]
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gdf[y] = gdf[y].ewm(10).mean()
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gdf.plot(
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x="global_step",
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y=y,
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label=f"{gtag}/{y}",
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ax=plt.gca(),
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marker="x" if len(gdf) < 100 else None,
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alpha=0.7,
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)
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plt.gca().legend(
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loc="center left",
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fancybox=True,
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shadow=True,
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bbox_to_anchor=(1.04, 0.5),
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)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("ys", nargs="+")
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parser.add_argument("--log-dir", default="logs", type=Path)
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parser.add_argument("--out-dir", default="logs", type=Path)
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parser.add_argument("--filename", default="log.txt")
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parser.add_argument("--max-x", type=float, default=float("inf"))
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parser.add_argument("--max-y", type=float, default=float("inf"))
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parser.add_argument("--group-level", default=1)
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parser.add_argument("--filter", default=None)
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args = parser.parse_args()
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paths = args.log_dir.rglob(f"**/{args.filename}")
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if args.filter:
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paths = filter(lambda p: re.match(".*" + args.filter + ".*", str(p)), paths)
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plot(paths, args)
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name = "-".join(args.ys)
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out_path = (args.out_dir / name).with_suffix(".png")
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plt.savefig(out_path, bbox_inches="tight")
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if __name__ == "__main__":
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main()
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import os
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import json
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for f in os.listdir(f'./data/librispeech_finetuning/1h/'):
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for j in os.listdir(f'./data/librispeech_finetuning/1h/{f}/clean'):
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for z in os.listdir(f'./data/librispeech_finetuning/1h/{f}/clean/{j}'):
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for i in os.listdir(f'./data/librispeech_finetuning/1h/{f}/clean/{j}/{z}'):
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os.rename(f'./data/librispeech_finetuning/1h/{f}/clean/{j}/{z}/{i}', f'./data/librilight-tts/{i}')
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for j in os.listdir('./data/librispeech_finetuning/9h/clean'):
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for z in os.listdir(f'./data/librispeech_finetuning/9h/clean/{j}'):
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for i in os.listdir(f'./data/librispeech_finetuning/9h/clean/{j}/{z}'):
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os.rename(f'./data/librispeech_finetuning/9h/clean/{j}/{z}/{i}', f'./data/librilight-tts/{i}')
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lst = []
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for i in os.listdir('./data/librilight-tts/'):
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try:
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if 'trans' not in i:
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continue
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with open(f'./data/librilight-tts/{i}') as f:
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for row in f:
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z = row.split('-')
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name = z[0]+'-'+z[1]+ '-' + z[2].split(' ')[0]
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text = " ".join(z[2].split(' ')[1:])
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lst.append([name, text])
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except Exception as e:
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pass
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for i in lst:
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try:
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with open(f'./data/librilight-tts/{i[0]}.txt', 'x') as file:
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file.write(i[1])
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except:
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with open(f'./data/librilight-tts/{i[0]}.txt', 'w+') as file:
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file.write(i[1])
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phoneme_map = {}
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phoneme_transcript = {}
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with open('./data/librispeech_finetuning/phones/phones_mapping.json', 'r') as f:
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phoneme_map_rev = json.load(f)
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for k, v in phoneme_map_rev.items():
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phoneme_map[f'{v}'] = k
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with open('./data/librispeech_finetuning/phones/10h_phones.txt', 'r') as f:
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lines = f.readlines()
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for line in lines:
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split = line.strip().split(" ")
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key = split[0]
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tokens = split[1:]
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phonemes = []
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for token in tokens:
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phoneme = phoneme_map[f'{token}']
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phonemes.append( phoneme )
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phoneme_transcript[key] = " ".join(phonemes)
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for filename in sorted(os.listdir('./data/librilight-tts')):
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split = filename.split('.')
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key = split[0]
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extension = split[1] # covers double duty of culling .normalized.txt and .phn.txt
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if extension != 'txt':
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continue
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os.rename(f'./data/librilight-tts/{filename}', f'./data/librilight-tts/{key}.normalized.txt')
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if key in phoneme_transcript:
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with open(f'./data/librilight-tts/{key}.phn.txt', 'w', encoding='utf-8') as f:
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f.write(phoneme_transcript[key])
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#!/bin/bash
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# do not invoke directly in scripts
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if [[ ${PWD##*/} == 'scripts' ]]; then
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cd ..
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fi
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# download training data
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cd data
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mkdir librilight-tts
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if [ ! -e ./librispeech_finetuning.tgz ]; then
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wget https://dl.fbaipublicfiles.com/librilight/data/librispeech_finetuning.tgz
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fi
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tar -xzf librispeech_finetuning.tgz
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cd ..
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# clean it up
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python3 ./scripts/prepare_libri.py
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# convert to wav
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pip3 install AudioConverter
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audioconvert convert ./data/librilight-tts/ ./data/librilight-tts --output-format .wav
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# process data
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ulimit -Sn `ulimit -Hn` # ROCm is a bitch
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python3 -m vall_e.emb.g2p ./data/librilight-tts # phonemizes anything that might have been amiss in the phoneme transcription
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python3 -m vall_e.emb.qnt ./data/librilight-tts
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import os
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import json
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for f in os.listdir(f'./LibriTTS/'):
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if not os.path.isdir(f'./LibriTTS/{f}/'):
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continue
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for j in os.listdir(f'./LibriTTS/{f}/'):
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if not os.path.isdir(f'./LibriTTS/{f}/{j}'):
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continue
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for z in os.listdir(f'./LibriTTS/{f}/{j}'):
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if not os.path.isdir(f'./LibriTTS/{f}/{j}/{z}'):
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continue
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for i in os.listdir(f'./LibriTTS/{f}/{j}/{z}'):
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if i[-4:] != ".wav":
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continue
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os.makedirs(f'./LibriTTS-Train/{j}/', exist_ok=True)
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os.rename(f'./LibriTTS/{f}/{j}/{z}/{i}', f'./LibriTTS-Train/{j}/{i}')
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