Update app.py

app.py

@@ -13,43 +13,6 @@ text_output = gr.TextArea(label="متن فارسی", type="text")
 
 processor = AutoProcessor.from_pretrained("SeyedAli/Persian-Speech-Transcription-Wav2Vec2-V1")
 model = AutoModelForCTC.from_pretrained("SeyedAli/Persian-Speech-Transcription-Wav2Vec2-V1")
-
-chars_to_ignore = [
- ",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�",
- "#", "!", "?", "«", "»", "(", ")", "؛", ",", "?", ".", "!", "-", ";", ":", '"', 
- "“", "%", "‘", "�", "–", "…", "_", "”", '“', '„'
-]
-chars_to_mapping = {
-"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
-}
-
-def multiple_replace(text, chars_to_mapping):
- pattern = "|".join(map(re.escape, chars_to_mapping.keys()))
- return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))
-
-def remove_special_characters(text, chars_to_ignore_regex):
- text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
- return text
-
-def normalizer(batch, chars_to_ignore, chars_to_mapping):
- chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]"""
- text = batch[0].lower().strip()
- 
- text = text.replace("\u0307", " ").strip()
- text = multiple_replace(text, chars_to_mapping)
- text = remove_special_characters(text, chars_to_ignore_regex)
-
- batch = text
- return batch
-
-
-def speech_file_to_array_fn(batch):
- speech_array, sampling_rate = torchaudio.load(batch["path"])
- speech_array = speech_array.squeeze().numpy()
- speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
-
- batch["speech"] = speech_array
- return batch
  
 def ASR(audio):
  pipe = pipeline("automatic-speech-recognition", model="SeyedAli/Persian-Speech-Transcription-Wav2Vec2-V1")
@@ -70,12 +33,7 @@ def ASR(audio):
  with torch.no_grad():
  logits = model(input_values,attention_mask).logits
  # Decode the transcription
- #result = normalizer(processor.batch_decode(torch.argmax(logits[0], dim=-1)),chars_to_ignore,chars_to_mapping)
  result = processor.decode(torch.argmax(logits[0], dim=-1))
- # max_items = np.random.randint(0, len(result), 10).tolist()
- # for i in max_items:
- # transcription=result[i]
- # return transcription
  return result
 iface = gr.Interface(fn=ASR, inputs=audio_input, outputs=text_output)
 iface.launch(share=False)