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app.py

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+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 rt_transcribe(audio, state=""):
+    time.sleep(2)
+    text = pipe(audio)["text"]
+    state += unicodedata.normalize("NFC",text) + " "
+    
+    return state, state
+
+
+
+demo = gr.Blocks()
+examples=[["examples/example1.mp3"], ["examples/example2.mp3"],["examples/example3.mp3"]]
+
+title ="""
+HindiSpeechPro: WAV2VEC-Powered ASR Interface
+"""
+
+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.
+<img src="https://huggingface.co/spaces/SakshiRathi77/SakshiRathi77-Wav2Vec2-hi-kagglex/resolve/main/Images/Hindi-Speech-Voice-Recognition-Tool.jpg" alt="logo" ;>
+</center>
+</p>
+"""
+
+# article = "<p style='text-align: center'><a href='https://github.com/SakshiRathi77/ASR' target='_blank'>Source Code on Github</a></p><p style='text-align: center'><a href='https://huggingface.co/blog/fine-tune-xlsr-wav2vec2' target='_blank'>Reference</a></p><p style='text-align: center'><a href='https://forms.gle/hjfc3F1P7m3weQVAA' target='_blank'><img src='https://e7.pngegg.com/pngimages/794/310/png-clipart-customer-review-feedback-user-service-others-miscellaneous-text-thumbnail.png' alt='Feedback Form' ;></a></p>"
+
+
+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=title,
+    description= description ,
+    allow_flagging="never",
+    examples=examples,
+)
+
+rt_transcribe = gr.Interface(
+    fn=rt_transcribe,
+    inputs=[
+        gr.Audio(source="microphone", type="filepath", streaming=True), 
+        "state"
+    ],
+    outputs=[ "textbox",
+        "state"],
+    theme="huggingface",
+    title=title,
+    description= description ,
+    allow_flagging="never",
+    live=True,
+)
+
+
+with demo:
+    gr.TabbedInterface([mf_transcribe, rt_transcribe], ["Transcribe Audio", "Transcribe Realtime Voice"])
+
+demo.launch(share=True)

packages.txt

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+ffmpeg

requirements.txt

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+torch
+git+https://github.com/huggingface/transformers
+pytube