Add first version of app

README.md

@@ -8,6 +8,10 @@ sdk_version: 4.42.0
 app_file: app.py
 pinned: false
 license: cc-by-nc-4.0
+tags:
+- age
+- gender
+- audio
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,144 @@
+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()

requirements.txt

@@ -0,0 +1,3 @@
+audiofile
+# torch
+transformers