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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch.optim as optim | |
| import torchaudio | |
| import sys | |
| import matplotlib.pyplot as plt | |
| import IPython.display as ipd | |
| from tqdm import tqdm | |
| import gradio as gr | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| sample_rate = 16000 | |
| new_sample_rate = 8000 | |
| transform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=new_sample_rate) | |
| class M5(nn.Module): | |
| def __init__(self, n_input=1, n_output=35, stride=16, n_channel=32): | |
| super().__init__() | |
| self.conv1 = nn.Conv1d(n_input, n_channel, kernel_size=80, stride=stride) | |
| self.bn1 = nn.BatchNorm1d(n_channel) | |
| self.pool1 = nn.MaxPool1d(4) | |
| self.conv2 = nn.Conv1d(n_channel, n_channel, kernel_size=3) | |
| self.bn2 = nn.BatchNorm1d(n_channel) | |
| self.pool2 = nn.MaxPool1d(4) | |
| self.conv3 = nn.Conv1d(n_channel, 2 * n_channel, kernel_size=3) | |
| self.bn3 = nn.BatchNorm1d(2 * n_channel) | |
| self.pool3 = nn.MaxPool1d(4) | |
| self.conv4 = nn.Conv1d(2 * n_channel, 2 * n_channel, kernel_size=3) | |
| self.bn4 = nn.BatchNorm1d(2 * n_channel) | |
| self.pool4 = nn.MaxPool1d(4) | |
| self.fc1 = nn.Linear(2 * n_channel, n_output) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = F.relu(self.bn1(x)) | |
| x = self.pool1(x) | |
| x = self.conv2(x) | |
| x = F.relu(self.bn2(x)) | |
| x = self.pool2(x) | |
| x = self.conv3(x) | |
| x = F.relu(self.bn3(x)) | |
| x = self.pool3(x) | |
| x = self.conv4(x) | |
| x = F.relu(self.bn4(x)) | |
| x = self.pool4(x) | |
| x = F.avg_pool1d(x, x.shape[-1]) | |
| x = x.permute(0, 2, 1) | |
| x = self.fc1(x) | |
| return F.log_softmax(x, dim=2) | |
| def get_likely_index(tensor): | |
| # find most likely label index for each element in the batch | |
| return tensor.argmax(dim=-1) | |
| def index_to_label(index): | |
| # Return the word corresponding to the index in labels | |
| # This is the inverse of label_to_index | |
| return labels[index] | |
| def predict(filepath): | |
| tensor=(torchaudio.load(filepath))[0] | |
| # Use the model to predict the label of the waveform | |
| tensor = tensor.to(device) | |
| tensor = transform(tensor) | |
| tensor = model(tensor.unsqueeze(0)) | |
| tensor = get_likely_index(tensor) | |
| tensor = index_to_label(tensor.squeeze()) | |
| return tensor | |
| def record(seconds=1): | |
| from google.colab import output as colab_output | |
| from base64 import b64decode | |
| from io import BytesIO | |
| from pydub import AudioSegment | |
| RECORD = ( | |
| b"const sleep = time => new Promise(resolve => setTimeout(resolve, time))\n" | |
| b"const b2text = blob => new Promise(resolve => {\n" | |
| b" const reader = new FileReader()\n" | |
| b" reader.onloadend = e => resolve(e.srcElement.result)\n" | |
| b" reader.readAsDataURL(blob)\n" | |
| b"})\n" | |
| b"var record = time => new Promise(async resolve => {\n" | |
| b" stream = await navigator.mediaDevices.getUserMedia({ audio: true })\n" | |
| b" recorder = new MediaRecorder(stream)\n" | |
| b" chunks = []\n" | |
| b" recorder.ondataavailable = e => chunks.push(e.data)\n" | |
| b" recorder.start()\n" | |
| b" await sleep(time)\n" | |
| b" recorder.onstop = async ()=>{\n" | |
| b" blob = new Blob(chunks)\n" | |
| b" text = await b2text(blob)\n" | |
| b" resolve(text)\n" | |
| b" }\n" | |
| b" recorder.stop()\n" | |
| b"})" | |
| ) | |
| RECORD = RECORD.decode("ascii") | |
| print(f"Recording started for {seconds} seconds.") | |
| display(ipd.Javascript(RECORD)) | |
| s = colab_output.eval_js("record(%d)" % (seconds * 1000)) | |
| print("Recording ended.") | |
| b = b64decode(s.split(",")[1]) | |
| fileformat = "wav" | |
| filename = f"_audio.{fileformat}" | |
| AudioSegment.from_file(BytesIO(b)).export(filename, format=fileformat) | |
| return torchaudio.load(filename) | |
| model = torch.load('export.pkl',map_location=torch.device('cpu')) | |
| gr.Interface(fn=predict, inputs=gr.inputs.Audio(source=record()[0]), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True) |