app.py

import gradio as gr
import torch
import torchaudio
from hydra.utils import instantiate
from src.models import AudioBatch

# Путь к чекпоинтам
CHECKPOINTS = {
    "RU+EN": "gigaam_ru_en/gigaam_ru_en.ckpt"
}

# Кэш моделей
LOADED_MODELS = {}

def load_model(ckpt_path):
    if ckpt_path in LOADED_MODELS:
        return LOADED_MODELS[ckpt_path]

    checkpoint = torch.load(ckpt_path, map_location='cpu')
    config = checkpoint['config']
    id2name = checkpoint['id2name']

    model = instantiate(config,  _recursive_=False)
    model.load_state_dict(checkpoint['state_dict'])
    model.eval()

    LOADED_MODELS[ckpt_path] = (model, id2name)
    return model, id2name

def classify_emotion(audio, ckpt_name):
    # Load waveform
    waveform, sr = torchaudio.load(audio)

    # Load model
    model, id2name = load_model(CHECKPOINTS[ckpt_name])

    # Если нужно, ресемплим до 16к
    if sr != 16000:
        waveform = torchaudio.transforms.Resample(orig_freq=sr, new_freq=16000)(waveform)

    # B x T
    if waveform.dim() > 1:
        waveform = waveform.mean(dim=0, keepdim=True)

    length = torch.tensor([waveform.shape[-1]])

    batch = AudioBatch(waveform, length, None)

    with torch.no_grad():
        logits, _ = model(batch)
        probs = torch.softmax(logits, dim=-1).squeeze(0)

    result = {label: float(probs[i]) for i, label in enumerate(id2name)}
    return result

demo = gr.Interface(
    fn=classify_emotion,
    inputs=[
        gr.Audio(type="filepath", label="Загрузите аудиофайл"),
        gr.Dropdown(choices=list(CHECKPOINTS.keys()), label="Выберите модель", value="RU+EN")
    ],
    outputs=gr.Label(label="Эмоциональная окраска (вероятности)"),
    title="Эмоциональная классификация речи (дообученная GigaAM на 8 классов)",
    description="Загрузите аудио"
)


demo.launch()