Adding requirements and applications files

app.py

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+import os
+import io
+import csv
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+import tensorflow_hub as hub
+import tensorflow_io as tfio
+import matplotlib.pyplot as plt
+from tensorflow import keras
+from huggingface_hub import from_pretrained_keras
+
+# Configuration
+class_names = [
+    "Irish",
+    "Midlands",
+    "Northern",
+    "Scottish",
+    "Southern",
+    "Welsh",
+    "Not a speech",
+]
+
+# Download Yamnet model from TF Hub
+yamnet_model = hub.load("https://tfhub.dev/google/yamnet/1")
+
+# Download dense model from HF Hub
+model = from_pretrained_keras(
+    pretrained_model_name_or_path="fbadine/uk_ireland_accent_classification"
+)
+
+# Function that reads a wav audio file and resamples it to 16000 Hz
+# This function is copied from the tutorial:
+# https://www.tensorflow.org/tutorials/audio/transfer_learning_audio
+def load_16k_audio_wav(filename):
+    # Read file content
+    file_content = tf.io.read_file(filename)
+
+    # Decode audio wave
+    audio_wav, sample_rate = tf.audio.decode_wav(file_content, desired_channels=1)
+    audio_wav = tf.squeeze(audio_wav, axis=-1)
+    sample_rate = tf.cast(sample_rate, dtype=tf.int64)
+
+    # Resample to 16k
+    audio_wav = tfio.audio.resample(audio_wav, rate_in=sample_rate, rate_out=16000)
+
+    return audio_wav
+
+
+# Function thatt takes the audio file produced by gr.Audio(source="microphone") and
+# returns a tensor applying the following transformations:
+# - Resample to 16000 Hz
+# - Normalize
+# - Reshape to [1, -1]
+def mic_to_tensor(recorded_audio_file):
+    sample_rate, audio = recorded_audio_file
+
+    audio_wav = tf.constant(audio, dtype=tf.float32)
+    if tf.rank(audio_wav) > 1:
+        audio_wav = tf.reduce_mean(audio_wav, axis=1)
+    audio_wav = tfio.audio.resample(audio_wav, rate_in=sample_rate, rate_out=16000)
+
+    audio_wav = tf.divide(audio_wav, tf.reduce_max(tf.abs(audio_wav)))
+
+    return audio_wav
+
+
+# Function that takes a tensor and applies the following:
+# - Pass it through Yamnet model to get the embeddings which are the input of the dense model
+# - Pass the embeddings through the dense model to get the predictions
+def tensor_to_predictions(audio_tensor):
+    # Get audio embeddings & scores.
+    scores, embeddings, mel_spectrogram = yamnet_model(audio_tensor)
+
+    # Predict the output of the accent recognition model with embeddings as input
+    predictions = model.predict(embeddings)
+
+    return predictions, mel_spectrogram
+
+
+# Function tha is called when the user clicks "Predict" button. It does the following:
+# - Calls tensor_to_predictions() to get the predictions
+# - Generates the top scoring labels
+# - Generates the top scoring plot
+def predict_accent(recorded_audio_file, uploaded_audio_file):
+    # Transform input to tensor
+    if recorded_audio_file:
+        audio_tensor = mic_to_tensor(recorded_audio_file)
+    else:
+        audio_tensor = load_16k_audio_wav(uploaded_audio_file)
+
+    # Model Inference
+    predictions, mel_spectrogram = tensor_to_predictions(audio_tensor)
+
+    # Get the infered class
+    infered_class = class_names[predictions.mean(axis=0).argmax()]
+
+    # Generate Output 1 - Accents
+    top_scoring_labels_output = {
+        class_names[i]: float(predictions.mean(axis=0)[i])
+        for i in range(len(class_names))
+    }
+
+    # Generate Output 2
+    top_scoring_plot_output = generate_top_scoring_plot(predictions)
+
+    return [top_scoring_labels_output, top_scoring_plot_output]
+
+
+# Clears all inputs and outputs when the user clicks "Clear" button
+def clear_inputs_and_outputs():
+    return [None, None, None, None]
+
+
+# Function that generates the top scoring plot
+# This function is copied from the tutorial and adjusted to our needs
+# https://keras.io/examples/audio/uk_ireland_accent_recognition/tinyurl.com/4a8xn7at
+def generate_top_scoring_plot(predictions):
+    # Plot and label the model output scores for the top-scoring classes.
+    mean_predictions = np.mean(predictions, axis=0)
+
+    top_class_indices = np.argsort(mean_predictions)[::-1]
+    fig = plt.figure(figsize=(10, 2))
+    plt.imshow(
+        predictions[:, top_class_indices].T,
+        aspect="auto",
+        interpolation="nearest",
+        cmap="gray_r",
+    )
+
+    # patch_padding = (PATCH_WINDOW_SECONDS / 2) / PATCH_HOP_SECONDS
+    # values from the model documentation
+    patch_padding = (0.025 / 2) / 0.01
+    plt.xlim([-patch_padding - 0.5, predictions.shape[0] + patch_padding - 0.5])
+    # Label the top_N classes.
+    yticks = range(0, len(class_names), 1)
+    plt.yticks(yticks, [class_names[top_class_indices[x]] for x in yticks])
+    _ = plt.ylim(-0.5 + np.array([len(class_names), 0]))
+
+    return fig
+
+
+# Main function
+if __name__ == "__main__":
+    demo = gr.Blocks()
+
+    with demo:
+        gr.Markdown(
+            """
+            <center><h1>English speaker accent recognition using Transfer Learning</h1></center> \
+            This space is a demo of an English (precisely UK & Ireland) accent classification model using Keras.<br> \
+    In this space, you can record your voice or upload a wav file and the model will predict the accent of the audio<br><br>
+            """
+        )
+        with gr.Row():
+            ## Input
+            with gr.Column():
+                mic_input = gr.Audio(source="microphone", label="Record your own voice")
+                upl_input = gr.Audio(
+                    source="upload", type="filepath", label="Upload a wav file"
+                )
+
+                with gr.Row():
+                    clr_btn = gr.Button(value="Clear", variant="secondary")
+                    prd_btn = gr.Button(value="Predict")
+
+            with gr.Column():
+                lbl_output = gr.Label(label="Top Predictions")
+                with gr.Group():
+                    gr.Markdown("<center>Prediction per time slot</center>")
+                    plt_output = gr.Plot(
+                        label="Prediction per time slot", show_label=False
+                    )
+
+        clr_btn.click(
+            fn=clear_inputs_and_outputs,
+            inputs=[],
+            outputs=[mic_input, upl_input, lbl_output, plt_output],
+        )
+        prd_btn.click(
+            fn=predict_accent,
+            inputs=[mic_input, upl_input],
+            outputs=[lbl_output, plt_output],
+        )
+
+    demo.launch(debug=True)

requirements.txt

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+numpy
+matplotlib
+tensorflow=2.8.0
+tensorflow_io=0.25.0
+tensorflow_hub=0.12.0