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README.md

@@ -1,8 +1,8 @@
 ---
-title: Speech Scope
-emoji: 🦀
-colorFrom: pink
-colorTo: purple
+title: Speech-Scope
+emoji: 🎤
+colorFrom: gray
+colorTo: indigo
 sdk: gradio
 sdk_version: 4.12.0
 app_file: app.py
@@ -10,4 +10,18 @@ pinned: false
 license: ecl-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Speech-Scope
+
+Análise de métricas de áudio.
+
+Confira a referência de configuração em [Hugging Face Spaces Config Reference](https://huggingface.co/docs/hub/spaces-config-reference).
+
+## Desenvolvedor
+
+Desenvolvido por Ramon Mayor Martins (2023)
+
+- E-mail: [rmayormartins@gmail.com](mailto:rmayormartins@gmail.com)
+- Homepage: [https://rmayormartins.github.io/](https://rmayormartins.github.io/)
+- Twitter: [@rmayormartins](https://twitter.com/rmayormartins)
+- GitHub: [https://github.com/rmayormartins](https://github.com/rmayormartins)
+

app.py

@@ -0,0 +1,83 @@
+import gradio as gr
+import librosa
+import numpy as np
+import matplotlib.pyplot as plt
+import tempfile
+import librosa.display
+
+
+def calculate_basic_metrics(y, sr):
+    pitches, magnitudes = librosa.piptrack(y=y, sr=sr)
+    average_pitch = np.mean(pitches[pitches > 0])
+    mfccs = librosa.feature.mfcc(y=y, sr=sr)
+    energy = np.sum(y ** 2)
+    zero_crossing_rate = np.mean(librosa.feature.zero_crossing_rate(y))
+    spectral_centroid = np.mean(librosa.feature.spectral_centroid(y=y, sr=sr))
+
+    return {
+        'Average Pitch': average_pitch,
+        'Number of MFCCs': mfccs.shape[1],
+        'Energy': energy,
+        'Zero Crossing Rate': zero_crossing_rate,
+        'Spectral Centroid': spectral_centroid
+    }
+
+
+def calculate_advanced_metrics(y, sr):
+    metrics = {}
+
+    f0, _, _ = librosa.pyin(y, fmin=50, fmax=4000)
+    if f0 is not None:
+        metrics['Average F0 (YIN)'] = np.nanmean(f0)
+
+    chroma = librosa.feature.chroma_stft(y=y, sr=sr)
+    metrics['Average Chroma'] = np.mean(chroma)
+
+    spectral_contrast = librosa.feature.spectral_contrast(y=y, sr=sr)
+    metrics['Average Spectral Contrast'] = np.mean(spectral_contrast)
+
+    return metrics
+
+
+def generate_spectrogram(y, sr):
+    plt.figure(figsize=(10, 4))
+    librosa.display.specshow(librosa.amplitude_to_db(np.abs(librosa.stft(y)), ref=np.max), sr=sr, x_axis='time', y_axis='log')
+    plt.colorbar(format='%+2.0f dB')
+    plt.title('Spectrogram')
+    plt.tight_layout()
+
+
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.png', mode='w+b') as f:
+        plt.savefig(f.name, format='png')
+        plt.close()
+        return f.name
+
+
+def process_audio(file):
+    if file is None:
+        return {}, "placeholder.png"  
+
+    sr, y = file
+
+    if y.dtype != np.float32:
+        y = y.astype(np.float32) / np.iinfo(y.dtype).max
+
+    basic_metrics = calculate_basic_metrics(y, sr)
+    advanced_metrics = calculate_advanced_metrics(y, sr)
+
+    metrics = {**basic_metrics, **advanced_metrics}
+
+    image_path = generate_spectrogram(y, sr)
+
+    return metrics, image_path
+
+
+iface = gr.Interface(
+    fn=process_audio,
+    inputs=gr.Audio(label="Upload Audio"),
+    outputs=["json", "image"],
+    title="Speech-Scope",
+    description="Speech and audio Metrics Analysis"
+)
+
+iface.launch(debug=True)

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+librosa
+numpy
+matplotlib