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import tempfile
import torch
import torch.nn.functional as F
import torchaudio
import gradio as gr
from transformers import Wav2Vec2FeatureExtractor, AutoConfig
from models import Wav2Vec2ForSpeechClassification, HubertForSpeechClassification
# Load model and feature extractor
config = AutoConfig.from_pretrained("Gizachew/wev2vec-large960-agu-amharic")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("Gizachew/wev2vec-large960-agu-amharic")
model = Wav2Vec2ForSpeechClassification.from_pretrained("Gizachew/wev2vec-large960-agu-amharic")
sampling_rate = feature_extractor.sampling_rate
# List of test examples (you can replace these with your actual file paths)
test_example = [
"test_examples/a5-06-02-02-60.wav",
"test_examples/f2-04-02-02-65.wav",
"test_examples/h3-06-02-02-41.wav",
"test_examples/n1-01-01-01-25.wav",
"test_examples/s4-06-01-02-51.wav"
]
# Define inputs and outputs for the Gradio interface
audio_input = gr.Audio(label="Upload file", type="filepath")
dropdown_input = gr.Dropdown(label="Choose an example audio file", choices=test_example)
text_output = gr.TextArea(label="Emotion Prediction Output", text_align="right", rtl=True, type="text")
def SER(audio):
with tempfile.NamedTemporaryFile(suffix=".wav") as temp_audio_file:
# Copy the contents of the uploaded audio file to the temporary file
temp_audio_file.write(open(audio, "rb").read())
temp_audio_file.flush()
# Load the audio file using torchaudio
speech_array, _sampling_rate = torchaudio.load(temp_audio_file.name)
resampler = torchaudio.transforms.Resample(_sampling_rate)
speech = resampler(speech_array).squeeze().numpy()
inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
inputs = {key: inputs[key] for key in inputs}
with torch.no_grad():
logits = model(**inputs).logits
scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
# Get the highest score and its corresponding label
max_index = scores.argmax()
label = config.id2label[max_index]
score = scores[max_index]
# Format the output string
output = f"{label}: {score * 100:.1f}%"
return output
def process_audio(audio_path):
return SER(audio_path)
# Create the Gradio interface
iface = gr.Interface(
fn=process_audio,
inputs=[audio_input, dropdown_input],
outputs=text_output
)
# Launch the Gradio app
iface.launch(share=True)
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