File size: 3,245 Bytes
3e40110 7a0a380 3e40110 7a0a380 3e40110 7a0a380 3e40110 7a0a380 3e40110 0bdcee2 3e40110 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
import pprint
from pathlib import Path
import gradio as gr
import librosa
import plotly.graph_objects as go
import spaces
import torch
from loguru import logger
from transformers import AutoFeatureExtractor
from transformers.modeling_outputs import SequenceClassifierOutput
from model import EmotionModel
repo_id = "my_model"
device = "cuda" if torch.cuda.is_available() else "cpu"
logger.info(f"device: {device}")
model = EmotionModel.from_pretrained(repo_id, device_map=device)
model.eval()
processor = AutoFeatureExtractor.from_pretrained(repo_id)
label_map = {
"Angry": "😠 怒り",
"Disgusted": "😒 嫌悪",
"Embarrassed": "😳 戸惑い",
"Fearful": "😨 恐怖",
"Happy": "😊 幸せ",
"Sad": "😢 悲しみ",
"Surprised": "😲 驚き",
"Neutral": "😐 中立",
"Sexual1": "🥰 NSFW1",
"Sexual2": "🍭 NSFW2",
}
@spaces.GPU
def pipe(filename: str) -> tuple[dict[str, float], go.Figure]:
if not filename:
return {"Error: ファイルが指定されていません": 0.0}, go.Figure()
logger.info(f"filename: {Path(filename).name}")
try:
y, sr = librosa.load(filename, mono=True, sr=16000)
except Exception as e:
# First convert to wav if librosa cannot read the file
logger.error(f"Error reading file: {e}")
from pydub import AudioSegment
segment = AudioSegment.from_file(filename)
segment.export("temp.wav", format="wav")
y, sr = librosa.load("temp.wav", mono=True, sr=16000)
Path("temp.wav").unlink()
duration = librosa.get_duration(y=y, sr=sr)
logger.info(f"Duration: {duration:.2f}s")
if duration > 30.0:
return (
{f"Error: 音声ファイルの長さが長すぎます: {duration:.2f}s": 0.0},
go.Figure(),
)
inputs = processor(y, sampling_rate=sr, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.no_grad():
outputs: SequenceClassifierOutput = model(**inputs)
logits = outputs.logits # shape: (batch_size, num_labels)
logits = logits[0].cpu().numpy()
labels = [label_map[label] for id, label in model.config.id2label.items()]
sorted_pairs = sorted(zip(logits, labels), key=lambda x: x[0])
sorted_logits, sorted_labels = zip(*sorted_pairs)
logger.info(f"Result:\n{pprint.pformat(sorted_pairs)}")
probabilities = outputs.logits.softmax(dim=-1)
scores_dict = {label: prob.item() for label, prob in zip(labels, probabilities[0])}
fig = go.Figure([go.Bar(x=sorted_logits, y=sorted_labels, orientation="h")])
return scores_dict, fig
md = """
# 音声からの感情認識 ver 0.1
- 音声ファイルから感情を予測して、確率とlogits (softmax前の値) を表示します
- 30秒以上の音声ファイルは受け付けません
- 声優による演技セリフ音声以外には微妙な結果になるかもしれません
"""
with gr.Blocks() as app:
gr.Markdown(md)
audio = gr.Audio(type="filepath")
btn = gr.Button("感情を予測")
with gr.Row():
result = gr.Label(label="結果")
plot = gr.Plot(label="Logits")
btn.click(pipe, inputs=audio, outputs=[result, plot])
app.launch()
|