File size: 4,765 Bytes
09aaa9c 2c19de2 09aaa9c 2c19de2 09aaa9c 2c19de2 09aaa9c 2c19de2 e2369f4 2c19de2 e2369f4 2c19de2 ff40bf8 2c19de2 ff40bf8 2c19de2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
# Import necessary libraries
import gradio as gr
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from transformers import WhisperModel, WhisperFeatureExtractor
import datasets
from datasets import load_dataset, DatasetDict, Audio
from huggingface_hub import PyTorchModelHubMixin
# Ensure you have the device setup (cuda or cpu)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Define data class
class SpeechInferenceDataset(Dataset):
def __init__(self, audio_data, text_processor):
self.audio_data = audio_data
self.text_processor = text_processor
def __len__(self):
return len(self.audio_data)
def __getitem__(self, index):
inputs = self.text_processor(self.audio_data[index]["audio"]["array"],
return_tensors="pt",
sampling_rate=self.audio_data[index]["audio"]["sampling_rate"])
input_features = inputs.input_features
decoder_input_ids = torch.tensor([[1, 1]]) # Modify as per your model's requirements
return input_features, decoder_input_ids
# Define model class
class SpeechClassifier(nn.Module, PyTorchModelHubMixin):
def __init__(self, config):
super(SpeechClassifier, self).__init__()
self.encoder = WhisperModel.from_pretrained(config["encoder"])
self.classifier = nn.Sequential(
nn.Linear(self.encoder.config.hidden_size, 4096),
nn.ReLU(),
nn.Linear(4096, 2048),
nn.ReLU(),
nn.Linear(2048, 1024),
nn.ReLU(),
nn.Linear(1024, 512),
nn.ReLU(),
nn.Linear(512, config["num_labels"])
)
def forward(self, input_features, decoder_input_ids):
outputs = self.encoder(input_features, decoder_input_ids=decoder_input_ids)
pooled_output = outputs['last_hidden_state'][:, 0, :]
logits = self.classifier(pooled_output)
return logits
# Prepare data function
def prepare_data(audio_file_path, model_checkpoint="openai/whisper-base"):
feature_extractor = WhisperFeatureExtractor.from_pretrained(model_checkpoint)
inference_data = datasets.Dataset.from_dict({"path": [audio_file_path], "audio": [audio_file_path]}).cast_column("audio", Audio(sampling_rate=16_000))
inference_dataset = SpeechInferenceDataset(inference_data, feature_extractor)
inference_loader = DataLoader(inference_dataset, batch_size=1, shuffle=False)
input_features, decoder_input_ids = next(iter(inference_loader))
input_features = input_features.squeeze(1).to(device)
decoder_input_ids = decoder_input_ids.squeeze(1).to(device)
return input_features, decoder_input_ids
# Prediction function
def predict(audio_file_path, config={"encoder": "openai/whisper-base", "num_labels": 2}):
input_features, decoder_input_ids = prepare_data(audio_file_path)
# Load the model from Hugging Face Hub
model = SpeechClassifier(config)
model.to(device)
# Use the correct method to load your model (this is an example and may not directly apply)
model.load_state_dict(torch.load(model.push_from_hub("jcho02/whisper_cleft")))
model.eval()
with torch.no_grad():
logits = model(input_features, decoder_input_ids)
predicted_ids = int(torch.argmax(logits, dim=-1))
return predicted_ids
# Gradio Interface function for uploaded files
def gradio_file_interface(uploaded_file):
with open(uploaded_file.name, "wb") as f:
f.write(uploaded_file.read())
prediction = predict(uploaded_file.name)
label = "Hypernasality Detected" if prediction == 1 else "No Hypernasality Detected"
return label
# Gradio Interface function for microphone input
def gradio_mic_interface(mic_input):
prediction = predict(mic_input.name)
label = "Hypernasality Detected" if prediction == 1 else "No Hypernasality Detected"
return label
# Initialize Blocks
demo = gr.Blocks()
# Define the interfaces inside the Blocks context
with demo:
mic_transcribe = gr.Interface(
fn=gradio_mic_interface,
inputs=gr.Audio(), # No type needed for microphone input
outputs=gr.Textbox(label="Prediction")
)
file_transcribe = gr.Interface(
fn=gradio_file_interface,
inputs=gr.Audio(type="filepath"), # Specify filepath for file upload
outputs=gr.Textbox(label="Prediction")
)
# Use a tabbed interface to switch between the microphone and file upload interfaces
gr.TabbedInterface([mic_transcribe, file_transcribe], ["Transcribe Microphone", "Transcribe Audio File"])
# Launch the demo with debugging enabled to catch any potential errors early on
demo.launch(debug=True)
|