Create app.py

app.py

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+import torch
+import torch.nn.functional as F
+
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+from transformers.models.whisper.tokenization_whisper import LANGUAGES
+from transformers.pipelines.audio_utils import ffmpeg_read
+
+import gradio as gr
+
+
+device = "cuda" if torch.cuda.is_available() else "CPU"
+
+model_ckpt = "ivanlau/language-detection-fine-tuned-on-xlm-roberta-base"
+model = AutoModelForSequenceClassification.from_pretrained(model_ckpt)
+tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
+
+def detect_language(sentence):
+    tokenized_sentence = tokenizer(sentence, return_tensors='pt')
+    output = model(**tokenized_sentence)
+    predictions = torch.nn.functional.softmax(output.logits, dim=-1)
+    probability, pred_idx = torch.max(predictions, dim=-1)
+    language = LANGUANGE_MAP[pred_idx.item()]
+    return language, probability.item()
+
+
+def process_audio_file(file):
+    with open(file, "rb") as f:
+        inputs = f.read()
+
+    audio = ffmpeg_read(inputs, sampling_rate)
+    return audio
+
+def transcribe(Microphone, File_Upload):
+    warn_output = ""
+    if (Microphone is not None) and (File_Upload is not None):
+        warn_output = "WARNING: You've uploaded an audio file and used the microphone. " \
+                      "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
+        file = Microphone
+
+    elif (Microphone is None) and (File_Upload is None):
+        return "ERROR: You have to either use the microphone or upload an audio file"
+
+    elif Microphone is not None:
+        file = Microphone
+    else:
+        file = File_Upload
+
+    audio_data = process_audio_file(file)
+
+    input_features = processor(audio_data, return_tensors="pt").input_features
+    
+    with torch.no_grad():
+        logits = model.forward(input_features.to(device), decoder_input_ids=decoder_input_ids).logits
+    
+    pred_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.decode(pred_ids[0])
+    
+    detect_language(transcription.capitalize())
+
+
+examples=['sample1.mp3', 'sample2.mp3', 'sample3.mp3']
+
+outputs=gr.outputs.Label(label="Language detected:")
+article = """
+Fine-tuned on xlm-roberta-base model.\n
+Supported languages:\n 
+    'Arabic', 'Basque', 'Breton', 'Catalan', 'Chinese_China', 'Chinese_Hongkong', 'Chinese_Taiwan', 'Chuvash', 'Czech', 
+    'Dhivehi', 'Dutch', 'English', 'Esperanto', 'Estonian', 'French', 'Frisian', 'Georgian', 'German', 'Greek', 'Hakha_Chin', 
+    'Indonesian', 'Interlingua', 'Italian', 'Japanese', 'Kabyle', 'Kinyarwanda', 'Kyrgyz', 'Latvian', 'Maltese', 
+    'Mangolian', 'Persian', 'Polish', 'Portuguese', 'Romanian', 'Romansh_Sursilvan', 'Russian', 'Sakha', 'Slovenian', 
+    'Spanish', 'Swedish', 'Tamil', 'Tatar', 'Turkish', 'Ukranian', 'Welsh'
+"""
+
+gr.Interface(
+    fn=detect_language,
+    fn=transcribe,
+    inputs=[
+        gr.inputs.Audio(source="microphone", type='filepath', optional=True),
+        gr.inputs.Audio(source="upload", type='filepath', optional=True),
+    ],
+    
+    outputs=outputs=[
+        gr.outputs.Textbox(label="Language"),
+        gr.Number(label="Probability"),
+    ],
+
+    verbose=True,
+    examples = examples,
+    title="Language Identification from Audio",
+    description="Detect the Language from Audio.",
+    article=article,
+    theme="huggingface"
+).launch()
+
+
+