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data_setups.py

@@ -25,7 +25,7 @@ def find_classes(directory: str):
     # 3. Crearte a dictionary of index labels (computers prefer numerical rather than string labels)
     class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
     return classes, class_to_idx
-    
+
 def resample(wav, sample_rate, new_sample_rate):
     if wav.shape[0] >= 2:
         wav = torch.mean(wav, dim=0)
@@ -61,9 +61,9 @@ def normalize(image, mean=None, std=None):
 def compute_melspec(wav, sample_rate=SAMPLE_RATE):
     melspec = librosa.feature.melspectrogram(
         y=wav,
-        sr=sample_rate, 
-        n_fft=N_FFT, 
-        fmin=F_MIN, 
+        sr=sample_rate,
+        n_fft=N_FFT,
+        fmin=F_MIN,
         fmax=F_MAX,
         n_mels=N_MELS,
         hop_length=HOP_LEN
@@ -77,4 +77,4 @@ def audio_preprocess(wav, sample_rate):
     image = mono_to_color(melspec)
     image = normalize(image, mean=None, std=None)
     image = torch.from_numpy(image)
-    return image
+    return image

requirements.txt

@@ -1,5 +1,5 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@de997e67c9a7feb9e2eccebf92969366dbd67eba#subdirectory=client/python
-https://gradio-builds.s3.amazonaws.com/de997e67c9a7feb9e2eccebf92969366dbd67eba/gradio-4.39.0-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@9b42ba8f1006c05d60a62450d3036ce0d6784f86#subdirectory=client/python
+https://gradio-builds.s3.amazonaws.com/9b42ba8f1006c05d60a62450d3036ce0d6784f86/gradio-4.39.0-py3-none-any.whl
 torch==1.12.0
 torchvision==0.13.0
 torchaudio==0.12.0

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: musical_instrument_identification\n", "### This demo identifies musical instruments from an audio file. It uses Gradio's Audio and Label components.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 librosa==0.9.2 gdown"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/musical_instrument_identification/data_setups.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "import torchaudio\n", "from timeit import default_timer as timer\n", "from data_setups import audio_preprocess, resample\n", "import gdown\n", "\n", "url = 'https://drive.google.com/uc?id=1X5CR18u0I-ZOi_8P0cNptCe5JGk9Ro0C'\n", "output = 'piano.wav'\n", "gdown.download(url, output, quiet=False)\n", "url = 'https://drive.google.com/uc?id=1W-8HwmGR5SiyDbUcGAZYYDKdCIst07__'\n", "output= 'torch_efficientnet_fold2_CNN.pth'\n", "gdown.download(url, output, quiet=False)\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "SAMPLE_RATE = 44100\n", "AUDIO_LEN = 2.90\n", "model = torch.load(\"torch_efficientnet_fold2_CNN.pth\", map_location=torch.device('cpu'))\n", "LABELS = [\n", "    \"Cello\", \"Clarinet\", \"Flute\", \"Acoustic Guitar\", \"Electric Guitar\", \"Organ\", \"Piano\", \"Saxophone\", \"Trumpet\", \"Violin\", \"Voice\"\n", "]\n", "example_list = [\n", "    [\"piano.wav\"]\n", "]\n", "\n", "\n", "def predict(audio_path):\n", "    start_time = timer()\n", "    wavform, sample_rate = torchaudio.load(audio_path)\n", "    wav = resample(wavform, sample_rate, SAMPLE_RATE)\n", "    if len(wav) > int(AUDIO_LEN * SAMPLE_RATE):\n", "        wav = wav[:int(AUDIO_LEN * SAMPLE_RATE)]\n", "    else:\n", "        print(f\"input length {len(wav)} too small!, need over {int(AUDIO_LEN * SAMPLE_RATE)}\")\n", "        return\n", "    img = audio_preprocess(wav, SAMPLE_RATE).unsqueeze(0)\n", "    model.eval()\n", "    with torch.inference_mode():\n", "        pred_probs = torch.softmax(model(img), dim=1)\n", "    pred_labels_and_probs = {LABELS[i]: float(pred_probs[0][i]) for i in range(len(LABELS))}\n", "    pred_time = round(timer() - start_time, 5)\n", "    return pred_labels_and_probs, pred_time\n", "\n", "demo = gr.Interface(fn=predict,\n", "                    inputs=gr.Audio(type=\"filepath\"),\n", "                    outputs=[gr.Label(num_top_classes=11, label=\"Predictions\"), \n", "                             gr.Number(label=\"Prediction time (s)\")],\n", "                    examples=example_list,\n", "                    cache_examples=False\n", "                    )\n", "\n", "demo.launch(debug=False)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: musical_instrument_identification\n", "### This demo identifies musical instruments from an audio file. It uses Gradio's Audio and Label components.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 librosa==0.9.2 gdown"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/musical_instrument_identification/data_setups.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "import torchaudio\n", "from timeit import default_timer as timer\n", "from data_setups import audio_preprocess, resample\n", "import gdown\n", "\n", "url = 'https://drive.google.com/uc?id=1X5CR18u0I-ZOi_8P0cNptCe5JGk9Ro0C'\n", "output = 'piano.wav'\n", "gdown.download(url, output, quiet=False)\n", "url = 'https://drive.google.com/uc?id=1W-8HwmGR5SiyDbUcGAZYYDKdCIst07__'\n", "output= 'torch_efficientnet_fold2_CNN.pth'\n", "gdown.download(url, output, quiet=False)\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "SAMPLE_RATE = 44100\n", "AUDIO_LEN = 2.90\n", "model = torch.load(\"torch_efficientnet_fold2_CNN.pth\", map_location=torch.device('cpu'))\n", "LABELS = [\n", "    \"Cello\", \"Clarinet\", \"Flute\", \"Acoustic Guitar\", \"Electric Guitar\", \"Organ\", \"Piano\", \"Saxophone\", \"Trumpet\", \"Violin\", \"Voice\"\n", "]\n", "example_list = [\n", "    [\"piano.wav\"]\n", "]\n", "\n", "def predict(audio_path):\n", "    start_time = timer()\n", "    wavform, sample_rate = torchaudio.load(audio_path)\n", "    wav = resample(wavform, sample_rate, SAMPLE_RATE)\n", "    if len(wav) > int(AUDIO_LEN * SAMPLE_RATE):\n", "        wav = wav[:int(AUDIO_LEN * SAMPLE_RATE)]\n", "    else:\n", "        print(f\"input length {len(wav)} too small!, need over {int(AUDIO_LEN * SAMPLE_RATE)}\")\n", "        return\n", "    img = audio_preprocess(wav, SAMPLE_RATE).unsqueeze(0)\n", "    model.eval()\n", "    with torch.inference_mode():\n", "        pred_probs = torch.softmax(model(img), dim=1)\n", "    pred_labels_and_probs = {LABELS[i]: float(pred_probs[0][i]) for i in range(len(LABELS))}\n", "    pred_time = round(timer() - start_time, 5)\n", "    return pred_labels_and_probs, pred_time\n", "\n", "demo = gr.Interface(fn=predict,\n", "                    inputs=gr.Audio(type=\"filepath\"),\n", "                    outputs=[gr.Label(num_top_classes=11, label=\"Predictions\"),\n", "                             gr.Number(label=\"Prediction time (s)\")],\n", "                    examples=example_list,\n", "                    cache_examples=False\n", "                    )\n", "\n", "demo.launch(debug=False)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -22,7 +22,6 @@ example_list = [
     ["piano.wav"]
 ]
 
-
 def predict(audio_path):
     start_time = timer()
     wavform, sample_rate = torchaudio.load(audio_path)
@@ -42,7 +41,7 @@ def predict(audio_path):
 
 demo = gr.Interface(fn=predict,
                     inputs=gr.Audio(type="filepath"),
-                    outputs=[gr.Label(num_top_classes=11, label="Predictions"), 
+                    outputs=[gr.Label(num_top_classes=11, label="Predictions"),
                              gr.Number(label="Prediction time (s)")],
                     examples=example_list,
                     cache_examples=False