Delete app_previous.py

app_previous.py

@@ -1,70 +0,0 @@
-import os
-import gradio as gr
-from pydub import AudioSegment
-import pyaudioconvert as pac
-import torch
-import torchaudio
-import sox
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor,Wav2Vec2ProcessorWithLM
-
-
-def convert (audio):
-  file_name = audio
-  if file_name.endswith("mp3") or file_name.endswith("wav") or file_name.endswith("ogg"):
-    if file_name.endswith("mp3"):
-      sound = AudioSegment.from_mp3(file_name)
-      sound.export(audio, format="wav")
-    elif file_name.endswith("ogg"):
-      sound = AudioSegment.from_ogg(audio)
-      sound.export(audio, format="wav")
-  else:
-    return False
-  pac.convert_wav_to_16bit_mono(audio,audio)
-  return True
-
-
-def parse_transcription_with_lm(logits):
-    result = processor_with_LM.batch_decode(logits.cpu().numpy())
-    text = result.text
-    transcription = text[0].replace('<s>','')
-    return transcription
-
-def parse_transcription(logits):
-    predicted_ids = torch.argmax(logits, dim=-1)
-    transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
-    return transcription
-
-def transcribe(audio_path, applyLM):
-    speech_array, sampling_rate = torchaudio.load(audio_path)
-    speech = torchaudio.functional.resample(speech_array, orig_freq=sampling_rate, new_freq=16000).squeeze().numpy()
-    """
-    if convert(audio_path)== False:
-        return "The format must be mp3,wav and ogg"
-    speech, sample_rate = torchaudio.load(audio_path)
-    """
-    
-    inputs = processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True)
-    with torch.no_grad():
-        logits = model(inputs.input_values).logits
-
-    if applyLM:
-        return parse_transcription_with_lm(logits)
-    else:
-        return parse_transcription(logits)
-
-auth_token = os.environ.get("key") or True
-model_id = "mutisya/wav2vec2-300m-kik-t22-1k-ft-withLM"
-processor = Wav2Vec2Processor.from_pretrained(model_id, use_auth_token=auth_token)
-processor_with_LM = Wav2Vec2ProcessorWithLM.from_pretrained(model_id, use_auth_token=auth_token)
-model = Wav2Vec2ForCTC.from_pretrained(model_id, use_auth_token=auth_token)
-
-
-gradio_ui = gr.Interface(
-    fn=transcribe,
-    title="Speech Recognition",
-    description="",
-    inputs=[gr.Audio(source="microphone", type="filepath", optional=True, label="Record from microphone"),
-            gr.Checkbox(label="Apply LM", value=False)],
-    outputs=[gr.outputs.Textbox(label="Recognized speech")]
-)
-gradio_ui.launch(enable_queue=True)