Epsilon617
update description
3462d76
import gradio as gr
#
from transformers import Wav2Vec2FeatureExtractor
from transformers import AutoModel
import torch
from torch import nn
import torchaudio
import torchaudio.transforms as T
import logging
import json
import os
import re
import pandas as pd
import importlib
modeling_MERT = importlib.import_module("MERT-v1-95M.modeling_MERT")
from Prediction_Head.MTGGenre_head import MLPProberBase
# input cr: https://huggingface.co/spaces/thealphhamerc/audio-to-text/blob/main/app.py
logger = logging.getLogger("MERT-v1-95M-app")
logger.setLevel(logging.INFO)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter(
"%(asctime)s;%(levelname)s;%(message)s", "%Y-%m-%d %H:%M:%S")
ch.setFormatter(formatter)
logger.addHandler(ch)
inputs = [
gr.components.Audio(type="filepath", label="Add music audio file"),
gr.inputs.Audio(source="microphone", type="filepath"),
]
live_inputs = [
gr.Audio(source="microphone",streaming=True, type="filepath"),
]
title = "One Model for All Music Understanding Tasks"
description = "An example of using the [MERT-v1-95M](https://huggingface.co/m-a-p/MERT-v1-95M) model as backbone to conduct multiple music understanding tasks with the universal represenation."
# article = "The tasks include EMO, GS, MTGInstrument, MTGGenre, MTGTop50, MTGMood, NSynthI, NSynthP, VocalSetS, VocalSetT. \n\n More models can be referred at the [map organization page](https://huggingface.co/m-a-p)."
with open('./README.md', 'r') as f:
# skip the header
header_count = 0
for line in f:
if '---' in line:
header_count += 1
if header_count >= 2:
break
# read the rest conent
article = f.read()
audio_examples = [
# ["input/example-1.wav"],
# ["input/example-2.wav"],
]
df_init = pd.DataFrame(columns=['Task', 'Top 1', 'Top 2', 'Top 3', 'Top 4', 'Top 5'])
transcription_df = gr.DataFrame(value=df_init, label="Output Dataframe", row_count=(
0, "dynamic"), max_rows=30, wrap=True, overflow_row_behaviour='paginate')
# outputs = [gr.components.Textbox()]
outputs = transcription_df
df_init_live = pd.DataFrame(columns=['Task', 'Top 1', 'Top 2', 'Top 3', 'Top 4', 'Top 5'])
transcription_df_live = gr.DataFrame(value=df_init_live, label="Output Dataframe", row_count=(
0, "dynamic"), max_rows=30, wrap=True, overflow_row_behaviour='paginate')
outputs_live = transcription_df_live
# Load the model and the corresponding preprocessor config
# model = AutoModel.from_pretrained("m-a-p/MERT-v0-public", trust_remote_code=True)
# processor = Wav2Vec2FeatureExtractor.from_pretrained("m-a-p/MERT-v0-public",trust_remote_code=True)
model = modeling_MERT.MERTModel.from_pretrained("./MERT-v1-95M")
processor = Wav2Vec2FeatureExtractor.from_pretrained("./MERT-v1-95M")
device = 'cuda' if torch.cuda.is_available() else 'cpu'
MERT_BEST_LAYER_IDX = {
'EMO': 5,
'GS': 8,
'GTZAN': 7,
'MTGGenre': 7,
'MTGInstrument': 'all',
'MTGMood': 6,
'MTGTop50': 6,
'MTT': 'all',
'NSynthI': 6,
'NSynthP': 1,
'VocalSetS': 2,
'VocalSetT': 9,
}
MERT_BEST_LAYER_IDX = {
'EMO': 5,
'GS': 8,
'GTZAN': 7,
'MTGGenre': 7,
'MTGInstrument': 'all',
'MTGMood': 6,
'MTGTop50': 6,
'MTT': 'all',
'NSynthI': 6,
'NSynthP': 1,
'VocalSetS': 2,
'VocalSetT': 9,
}
CLASSIFIERS = {
}
ID2CLASS = {
}
TASKS = ['GS', 'MTGInstrument', 'MTGGenre', 'MTGTop50', 'MTGMood', 'NSynthI', 'NSynthP', 'VocalSetS', 'VocalSetT','EMO',]
Regression_TASKS = ['EMO']
head_dir = './Prediction_Head/best-layer-MERT-v1-95M'
for task in TASKS:
print('loading', task)
with open(os.path.join(head_dir,f'{task}.id2class.json'), 'r') as f:
ID2CLASS[task]=json.load(f)
num_class = len(ID2CLASS[task].keys())
CLASSIFIERS[task] = MLPProberBase(d=768, layer=MERT_BEST_LAYER_IDX[task], num_outputs=num_class)
CLASSIFIERS[task].load_state_dict(torch.load(f'{head_dir}/{task}.ckpt')['state_dict'])
CLASSIFIERS[task].to(device)
model.to(device)
def model_infernce(inputs):
waveform, sample_rate = torchaudio.load(inputs)
resample_rate = processor.sampling_rate
# make sure the sample_rate aligned
if resample_rate != sample_rate:
# print(f'setting rate from {sample_rate} to {resample_rate}')
resampler = T.Resample(sample_rate, resample_rate)
waveform = resampler(waveform)
waveform = waveform.view(-1,) # make it (n_sample, )
model_inputs = processor(waveform, sampling_rate=resample_rate, return_tensors="pt")
model_inputs.to(device)
with torch.no_grad():
model_outputs = model(**model_inputs, output_hidden_states=True)
# take a look at the output shape, there are 13 layers of representation
# each layer performs differently in different downstream tasks, you should choose empirically
all_layer_hidden_states = torch.stack(model_outputs.hidden_states).squeeze()[1:,:,:].unsqueeze(0)
print(all_layer_hidden_states.shape) # [13 layer, Time steps, 768 feature_dim]
all_layer_hidden_states = all_layer_hidden_states.mean(dim=2)
task_output_texts = ""
df = pd.DataFrame(columns=['Task', 'Top 1', 'Top 2', 'Top 3', 'Top 4', 'Top 5'])
df_objects = []
for task in TASKS:
num_class = len(ID2CLASS[task].keys())
if MERT_BEST_LAYER_IDX[task] == 'all':
logits = CLASSIFIERS[task](all_layer_hidden_states) # [1, 87]
else:
logits = CLASSIFIERS[task](all_layer_hidden_states[:, MERT_BEST_LAYER_IDX[task]])
# print(f'task {task} logits:', logits.shape, 'num class:', num_class)
sorted_idx = torch.argsort(logits, dim = -1, descending=True)[0] # batch =1
sorted_prob,_ = torch.sort(nn.functional.softmax(logits[0], dim=-1), dim=-1, descending=True)
# print(sorted_prob)
# print(sorted_prob.shape)
top_n_show = 5 if num_class >= 5 else num_class
# task_output_texts = task_output_texts + f"TASK {task} output:\n" + "\n".join([str(ID2CLASS[task][str(sorted_idx[idx].item())])+f', probability: {sorted_prob[idx].item():.2%}' for idx in range(top_n_show)]) + '\n'
# task_output_texts = task_output_texts + '----------------------\n'
row_elements = [task]
for idx in range(top_n_show):
print(ID2CLASS[task])
# print('id', str(sorted_idx[idx].item()))
output_class_name = str(ID2CLASS[task][str(sorted_idx[idx].item())])
output_class_name = re.sub(r'^\w+---', '', output_class_name)
output_class_name = re.sub(r'^\w+\/\w+---', '', output_class_name)
# print('output name', output_class_name)
output_prob = f' {sorted_prob[idx].item():.2%}'
row_elements.append(output_class_name+output_prob)
# fill empty elment
for _ in range(5+1 - len(row_elements)):
row_elements.append(' ')
df_objects.append(row_elements)
df = pd.DataFrame(df_objects, columns=['Task', 'Top 1', 'Top 2', 'Top 3', 'Top 4', 'Top 5'])
return df
def convert_audio(inputs, microphone):
if (microphone is not None):
inputs = microphone
df = model_infernce(inputs)
return df
def live_convert_audio(microphone):
if (microphone is not None):
inputs = microphone
df = model_infernce(inputs)
return df
audio_chunked = gr.Interface(
fn=convert_audio,
inputs=inputs,
outputs=outputs,
allow_flagging="never",
title=title,
description=description,
article=article,
examples=audio_examples,
)
live_audio_chunked = gr.Interface(
fn=live_convert_audio,
inputs=live_inputs,
outputs=outputs_live,
allow_flagging="never",
title=title,
description=description,
article=article,
# examples=audio_examples,
live=True,
)
demo = gr.Blocks()
with demo:
gr.TabbedInterface(
[
audio_chunked,
live_audio_chunked,
],
[
"Audio File or Recording",
"Live Streaming Music"
]
)
# demo.queue(concurrency_count=1, max_size=5)
demo.launch(show_api=False)