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import glob
import os
import shutil
import sys
import re
import tempfile
import zipfile
from pathlib import Path
import gradio as gr
from finetune import finetune_model, baseline_model
from language import languages
from task import tasks
import matplotlib.pyplot as plt
os.environ['TEMP_DIR'] = tempfile.mkdtemp()
def load_markdown():
with open("intro.md", "r") as f:
return f.read()
def read_logs():
try:
with open(f"output.log", "r") as f:
return f.read()
except:
return None
def plot_loss_acc(temp_dir, log_every):
sys.stdout.flush()
lines = []
with open("output.log", "r") as f:
for line in f.readlines():
if re.match(r"^\[\d+\] - loss: \d+\.\d+ - acc: \d+\.\d+$", line):
lines.append(line)
losses = []
acces = []
if len(lines) == 0:
return None, None
for line in lines:
_, loss, acc = line.split(" - ")
losses.append(float(loss.split(":")[1].strip()))
acces.append(float(acc.split(":")[1].strip()))
x = [i * log_every for i in range(1, len(losses) + 1)]
plt.plot(x, losses, label="loss")
plt.xlim(log_every // 2, x[-1] + log_every // 2)
plt.savefig(f"{temp_dir}/loss.png")
plt.clf()
plt.plot(x, acces, label="acc")
plt.xlim(log_every // 2, x[-1] + log_every // 2)
plt.savefig(f"{temp_dir}/acc.png")
plt.clf()
return f"{temp_dir}/acc.png", f"{temp_dir}/loss.png"
def upload_file(fileobj, temp_dir):
"""
Upload a file and check the uploaded zip file.
"""
# First check if a file is a zip file.
if not zipfile.is_zipfile(fileobj.name):
raise gr.Error("Please upload a zip file.")
# Then unzip file
shutil.unpack_archive(fileobj.name, temp_dir)
# check zip file
if not os.path.exists(os.path.join(temp_dir, "text")):
raise gr.Error("Please upload a valid zip file.")
if not os.path.exists(os.path.join(temp_dir, "text_ctc")):
raise gr.Error("Please upload a valid zip file.")
if not os.path.exists(os.path.join(temp_dir, "audio")):
raise gr.Error("Please upload a valid zip file.")
# check if all texts and audio matches
audio_ids = []
with open(os.path.join(temp_dir, "text"), "r") as f:
for line in f.readlines():
audio_ids.append(line.split(maxsplit=1)[0])
with open(os.path.join(temp_dir, "text_ctc"), "r") as f:
ctc_audio_ids = []
for line in f.readlines():
ctc_audio_ids.append(line.split(maxsplit=1)[0])
if len(audio_ids) != len(ctc_audio_ids):
raise gr.Error(
f"Length of `text` ({len(audio_ids)}) and `text_ctc` ({len(ctc_audio_ids)}) is different."
)
if set(audio_ids) != set(ctc_audio_ids):
raise gr.Error(f"`text` and `text_ctc` have different audio ids.")
for audio_id in glob.glob(os.path.join(temp_dir, "audio", "*")):
if not Path(audio_id).stem in audio_ids:
raise gr.Error(f"Audio id {audio_id} is not in `text` or `text_ctc`.")
gr.Info("Successfully uploaded and validated zip file.")
return [fileobj]
with gr.Blocks(title="OWSM-finetune") as demo:
tempdir_path = gr.State(os.environ['TEMP_DIR'])
gr.Markdown(
"""# OWSM finetune demo!
Finetune `owsm_v3.1_ebf_base` with your own dataset!
Due to resource limitation, you can only train 50 epochs on maximum.
## Upload dataset and define settings
"""
)
# main contents
with gr.Row():
with gr.Column():
file_output = gr.File()
upload_button = gr.UploadButton("Click to Upload a File", file_count="single")
upload_button.upload(
upload_file, [upload_button, tempdir_path], [file_output]
)
with gr.Column():
lang = gr.Dropdown(
languages["espnet/owsm_v3.1_ebf_base"],
label="Language",
info="Choose language!",
value="jpn",
interactive=True,
)
task = gr.Dropdown(
tasks["espnet/owsm_v3.1_ebf_base"],
label="Task",
info="Choose task!",
value="asr",
interactive=True,
)
gr.Markdown("## Set training settings")
with gr.Row():
with gr.Column():
log_every = gr.Number(value=10, label="log_every", interactive=True)
max_epoch = gr.Slider(1, 10, step=1, label="max_epoch", interactive=True)
scheduler = gr.Dropdown(
["warmuplr"], label="warmup", value="warmuplr", interactive=True
)
warmup_steps = gr.Number(
value=100, label="warmup_steps", interactive=True
)
with gr.Column():
optimizer = gr.Dropdown(
["adam", "adamw", "sgd", "adadelta", "adagrad", "adamax", "asgd", "rmsprop"],
label="optimizer",
value="adam",
interactive=True
)
learning_rate = gr.Number(
value=1e-4, label="learning_rate", interactive=True
)
weight_decay = gr.Number(
value=0.000001, label="weight_decay", interactive=True
)
gr.Markdown("## Logs and plots")
with gr.Row():
with gr.Column():
log_output = gr.Textbox(
show_label=False,
interactive=False,
max_lines=23,
lines=23,
)
demo.load(read_logs, None, log_output, every=2)
with gr.Column():
log_acc = gr.Image(label="Accuracy", show_label=True, interactive=False)
log_loss = gr.Image(label="Loss", show_label=True, interactive=False)
demo.load(plot_loss_acc, [tempdir_path, log_every], [log_acc, log_loss], every=10)
with gr.Row():
with gr.Column():
ref_text = gr.Textbox(
label="Reference text",
show_label=True,
interactive=False,
max_lines=10,
lines=10,
)
with gr.Column():
base_text = gr.Textbox(
label="Baseline text",
show_label=True,
interactive=False,
max_lines=10,
lines=10,
)
with gr.Row():
with gr.Column():
hyp_text = gr.Textbox(
label="Hypothesis text",
show_label=True,
interactive=False,
max_lines=10,
lines=10,
)
with gr.Column():
trained_model = gr.File(
label="Trained model",
interactive=False,
)
with gr.Row():
with gr.Column():
baseline_btn = gr.Button("Run Baseline", variant="secondary")
baseline_btn.click(
baseline_model,
[
lang,
task,
tempdir_path,
],
[ref_text, base_text]
)
with gr.Column():
finetune_btn = gr.Button("Finetune Model", variant="primary")
finetune_btn.click(
finetune_model,
[
lang,
task,
tempdir_path,
log_every,
max_epoch,
scheduler,
warmup_steps,
optimizer,
learning_rate,
weight_decay,
],
[trained_model, hyp_text]
)
gr.Markdown(load_markdown())
if __name__ == "__main__":
try:
demo.queue().launch()
except:
print("Unexpected error:", sys.exc_info()[0])
raise
finally:
shutil.rmtree(os.environ['TEMP_DIR'])
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