Update app.py

app.py

@@ -1,3 +1,91 @@
+import torch
 import gradio as gr
+import pytube as pt
+from transformers import pipeline
+from huggingface_hub import model_info
+import time
+import unicodedata
+
+MODEL_NAME = "SakshiRathi77/wav2vec2-large-xlsr-300m-hi-kagglex"
+lang = "hi"
+
+device = 0 if torch.cuda.is_available() else "cpu"
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=MODEL_NAME,
+    device=device,
+)
+
+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"
+        )
+
+    elif (microphone is None) and (file_upload is None):
+        return "ERROR: You have to either use the microphone or upload an audio file"
+    file = microphone if microphone is not None else file_upload
+    text = pipe(file)["text"]
+    
+    return warn_output + text
+
+
+# def _return_yt_html_embed(yt_url):
+#     video_id = yt_url.split("?v=")[-1]
+#     HTML_str = (
+#         f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
+#         " </center>"
+#     )
+#     return HTML_str
+
+
+# def yt_transcribe(yt_url):
+#     yt = pt.YouTube(yt_url)
+#     html_embed_str = _return_yt_html_embed(yt_url)
+#     stream = yt.streams.filter(only_audio=True)[0]
+#     stream.download(filename="audio.mp3")
+
+#     text = pipe("audio.mp3")["text"]
+
+#     return html_embed_str, text
+
+def rt_transcribe(audio, state=""):
+    time.sleep(2)
+    text = p(audio)["text"]
+    state += unicodedata.normalize("NFC",text) + " "
+    return state, state
+
+
+demo = gr.Blocks()
+
+examples=[["examples/example1.mp3"], ["examples/example2.mp3"]]
+
+description = """
+<p>
+<center>
+Welcome to the HindiSpeechPro, a cutting-edge interface powered by a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. Easily convert your spoken words to accurate text with just a few clicks.
+</center>
+</p>
+<center>
+<img src="https://huggingface.co/spaces/kingabzpro/real-time-Urdu-ASR/resolve/main/Images/cover.jpg" alt="logo" width="550"/>
+</center>
+"""
+
+mf_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.inputs.Audio(source="microphone", type="filepath"),
+        gr.inputs.Audio(source="upload", type="filepath"),
+    ],
+    outputs="text",
+    theme="huggingface",
+    title="HindiSpeechPro: WAV2VEC-Powered ASR Interface",
+    description= description ,
+    allow_flagging="never",
+    examples=examples,
+).launch(share=True )
+
 
 gr.Interface.load("models/SakshiRathi77/wav2vec2-large-xlsr-300m-hi-kagglex").launch()