app.py

import gradio as gr
import numpy as np
import spaces
import torch
import torch.nn as nn
from transformers import Wav2Vec2Processor
from transformers.models.wav2vec2.modeling_wav2vec2 import Wav2Vec2Model
from transformers.models.wav2vec2.modeling_wav2vec2 import Wav2Vec2PreTrainedModel

import audiofile


class ModelHead(nn.Module):
    r"""Classification head."""

    def __init__(self, config, num_labels):

        super().__init__()

        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.final_dropout)
        self.out_proj = nn.Linear(config.hidden_size, num_labels)

    def forward(self, features, **kwargs):

        x = features
        x = self.dropout(x)
        x = self.dense(x)
        x = torch.tanh(x)
        x = self.dropout(x)
        x = self.out_proj(x)

        return x


class AgeGenderModel(Wav2Vec2PreTrainedModel):
    r"""Speech emotion classifier."""

    def __init__(self, config):

        super().__init__(config)

        self.config = config
        self.wav2vec2 = Wav2Vec2Model(config)
        self.age = ModelHead(config, 1)
        self.gender = ModelHead(config, 3)
        self.init_weights()

    def forward(
            self,
            input_values,
    ):

        outputs = self.wav2vec2(input_values)
        hidden_states = outputs[0]
        hidden_states = torch.mean(hidden_states, dim=1)
        logits_age = self.age(hidden_states)
        logits_gender = torch.softmax(self.gender(hidden_states), dim=1)

        return hidden_states, logits_age, logits_gender



# load model from hub
device = 0 if torch.cuda.is_available() else "cpu"
model_name = "audeering/wav2vec2-large-robust-24-ft-age-gender"
processor = Wav2Vec2Processor.from_pretrained(model_name)
model = AgeGenderModel.from_pretrained(model_name)


def process_func(x: np.ndarray, sampling_rate: int) -> dict:
    r"""Predict age and gender or extract embeddings from raw audio signal."""
    # run through processor to normalize signal
    # always returns a batch, so we just get the first entry
    # then we put it on the device
    y = processor(x, sampling_rate=sampling_rate)
    y = y['input_values'][0]
    y = y.reshape(1, -1)
    y = torch.from_numpy(y).to(device)

    # run through model
    with torch.no_grad():
        y = model(y)
        y = torch.hstack([y[1], y[2]])

    # convert to numpy
    y = y.detach().cpu().numpy()

    # convert to dict
    y = [
        {"score": 100 * y[0][0], "label": "age"},
        {"score": y[0][1], "label": "female"},
        {"score": y[0][2], "label": "male"},
        {"score": y[0][3], "label": "child"},
    ]

    return y


@spaces.GPU
def recognize(file):
    if file is None:
        raise gr.Error(
            "No audio file submitted! "
            "Please upload or record an audio file "
            "before submitting your request."
        )
    signal, sampling_rate = audiofile.read(file)
    age_gender = process_func(signal, sampling_rate)
    return  age_gender


demo = gr.Blocks()

outputs = gr.outputs.Label()
title = "audEERING age and gender recognition"
description = (
    "Recognize age and gender of a microphone recording or audio file. "
    "Demo uses the checkpoint [{model_name}](https://huggingface.co/{model_name})."
)
allow_flagging = "never"

microphone = gr.Interface(
    fn=recognize,
    inputs=gr.Audio(sources="microphone", type="filepath"),
    outputs=outputs,
    title=title,
    description=description,
    allow_flagging=allow_flagging,
)

file = gr.Interface(
    fn=recognize,
    inputs=gr.Audio(sources="upload", type="filepath", label="Audio file"),
    outputs=outputs,
    title=title,
    description=description,
    allow_flagging=allow_flagging,
)

with demo:
    gr.TabbedInterface([microphone, file], ["Microphone", "Audio file"])

demo.queue().launch()