Update README.md

README.md

@@ -1,3 +1,52 @@
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-license: cc-by-sa-4.0
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+---
+license: cc-by-sa-4.0
+---
+# Detect Profanity in Surabaya Javanese Dialect
+This is the model built for the project
+[Deteksi Perkataan Vulgar Dalam Bahasa Jawa Dialek Surabaya Pada Konten Video Dengan Speech-To-Text ](https://github.com/jaal047/Detect-Profanity-in-Surabaya-Javanese-Dialect)
+
+It is a fine-tuned [indonesian-nlp/wav2vec2-indonesian-javanese-sundanese](https://huggingface.co/indonesian-nlp/wav2vec2-indonesian-javanese-sundanese)
+model on the [Profanity Speech Suroboyoan dataset](https://huggingface.co/datasets/Jaal047/profanity-speech-suroboyoan)
+
+When using this model, make sure that your speech input is sampled at 16kHz.
+
+## Usage
+The model can be used directly (without a language model) as follows:
+```python
+import torch
+import torchaudio
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import noisereduce as nr
+import librosa
+import soundfile as sf
+
+# Load model dan processor
+processor = Wav2Vec2Processor.from_pretrained("Jaal047/profanity-javanese-sby")
+model = Wav2Vec2ForCTC.from_pretrained("Jaal047/profanity-javanese-sby")
+
+# Load dan kurangi noise dari audio
+file_audio_path = 'audio.wav'
+y, sr = librosa.load(file_audio_path, sr=16000)
+reduced_noise = nr.reduce_noise(y=y, sr=sr)
+sf.write('audio_reduced_noise1.wav', reduced_noise, sr)
+
+# Fungsi untuk memuat dan preprocess audio
+def load_and_preprocess_audio(file_path):
+    audio_array, sampling_rate = torchaudio.load(file_path)
+    if sampling_rate != 16000:
+        audio_array = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16000)(audio_array)
+    audio_array = torchaudio.transforms.Vol(gain=1.0, gain_type='amplitude')(audio_array)
+    return audio_array.squeeze().numpy()
+
+# Preprocess dan inferensi
+audio_array = load_and_preprocess_audio('audio_reduced_noise1.wav')
+inputs = processor(audio_array, sampling_rate=16000, return_tensors="pt", padding=True)
+with torch.no_grad():
+    logits = model(inputs.input_values).logits
+
+# Ambil argmax dan decode prediksi
+predicted_ids = torch.argmax(logits, dim=-1)
+transcription = processor.batch_decode(predicted_ids)[0]
+
+print("Transkripsi:", transcription)
+```