Delete app.py

app.py

@@ -1,83 +0,0 @@
-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
-
-
-model_id = "mageec/whisper-tiny-hi-capstone"
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-processor = WhisperProcessor.from_pretrained(model_id)
-model = WhisperForConditionalGeneration.from_pretrained(model_id)
-model.eval()
-model.to(device)
-
-sampling_rate = processor.feature_extractor.sampling_rate
-
-bos_token_id = processor.tokenizer.all_special_ids[-106]
-decoder_input_ids = torch.tensor([1,bos_token_id]).to(device)
-
-
-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)
-    probability = F.softmax(logits, dim=-1).max()
-
-    lang_ids = processor.decode(pred_ids[0])
-    
-    lang_ids = lang_ids.lstrip("<|").rstrip("|>")
-    language = LANGUAGES.get(lang_ids, "not detected")
-
-    return language.capitalize(), probability.cpu().numpy()
-
-
-iface = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.inputs.Audio(source="microphone", type='filepath', optional=True),
-        gr.inputs.Audio(source="upload", type='filepath', optional=True),
-    ],
-    outputs=[
-        gr.outputs.Textbox(label="Language"),
-        gr.Number(label="Probability"),
-    ],
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Language Identification",
-    description="Demo for Language Identification using OpenAI's [Whisper Large V2](https://huggingface.co/openai/whisper-large-v2).",
-    allow_flagging='never',
-)
-iface.launch(enable_queue=True)