gradio

app.py

@@ -1,25 +1,44 @@
+import gradio as gr
 import librosa
 import torch
-from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2ForCTC, Wav2Vec2Processor, TrainingArguments, Wav2Vec2FeatureExtractor, Trainer
+from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2ForCTC, Wav2Vec2Processor
 
+# Load tokenizer, processor, and model
 tokenizer = Wav2Vec2CTCTokenizer("./vocab.json", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|")
 processor = Wav2Vec2Processor.from_pretrained('boumehdi/wav2vec2-large-xlsr-moroccan-darija', tokenizer=tokenizer)
-model=Wav2Vec2ForCTC.from_pretrained('boumehdi/wav2vec2-large-xlsr-moroccan-darija')
-
-
-# load the audio data (use your own wav file here!)
-input_audio, sr = librosa.load('file.wav', sr=16000)
-
-# tokenize
-input_values = processor(input_audio, return_tensors="pt", padding=True).input_values
-
-# retrieve logits
-logits = model(input_values).logits
-
-tokens = torch.argmax(logits, axis=-1)
-
-# decode using n-gram
-transcription = tokenizer.batch_decode(tokens)
-
-# print the output
-print(transcription)
+model = Wav2Vec2ForCTC.from_pretrained('boumehdi/wav2vec2-large-xlsr-moroccan-darija')
+
+# Define the function for transcribing audio
+def transcribe(audio):
+    # Load the audio data from the Gradio input (audio is in the format of a NumPy array)
+    input_audio = audio
+    sr = 16000  # Ensure the sample rate is 16000 Hz, which is expected by the model
+
+    # Tokenize the audio
+    input_values = processor(input_audio, return_tensors="pt", padding=True).input_values
+
+    # Get the model's logits
+    logits = model(input_values).logits
+
+    # Find the predicted tokens
+    tokens = torch.argmax(logits, axis=-1)
+
+    # Decode the tokens to text
+    transcription = tokenizer.batch_decode(tokens)
+
+    return transcription[0]
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=transcribe,  # Function to be called when an audio file is uploaded or recorded
+    inputs=[
+        gr.Audio(source="upload", type="numpy"),  # Allow user to upload an audio file
+        gr.Audio(source="microphone", type="numpy")  # Allow user to record audio from the browser
+    ],
+    outputs="text",  # Output will be a transcription text
+    title="Moroccan Darija Speech-to-Text",  # Interface title
+    description="Upload an audio file or record audio directly from your microphone to transcribe it into Moroccan Darija."
+)
+
+# Launch the interface
+interface.launch()