Create app.py

app.py

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+import soundfile as sf
+import torch
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import gradio as gr
+import sox
+import subprocess
+
+
+def read_file_and_process(wav_file):
+    filename = wav_file.split('.')[0]
+    filename_16k = filename + "16k.wav"
+    resampler(wav_file, filename_16k)
+    speech, _ = sf.read(filename_16k)
+    inputs = processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True)
+    
+    return inputs
+
+
+def resampler(input_file_path, output_file_path):
+    command = (
+        f"ffmpeg -hide_banner -loglevel panic -i {input_file_path} -ar 16000 -ac 1 -bits_per_raw_sample 16 -vn "
+        f"{output_file_path}"
+    )
+    subprocess.call(command, shell=True)
+
+
+def parse_transcription(logits):
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
+    return transcription
+
+
+def parse(wav_file):
+    input_values = read_file_and_process(wav_file)
+    with torch.no_grad():
+        logits = model(**input_values).logits
+    return parse_transcription(logits)
+
+model_id = "SeyedAli/Persian-Speech-Transcription-Wav2Vec2-V1"
+processor = Wav2Vec2Processor.from_pretrained(model_id)
+model = Wav2Vec2ForCTC.from_pretrained(model_id)
+
+input_ = gr.Audio(source="microphone", type="filepath")
+txtbox = gr.Textbox(
+            label="persian text output:",
+            lines=5
+        )
+
+title = "Speech-to-Text (persian)"
+description = "Upload a prsian audio, and let AI do the hard work of transcribing."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2104.06678'>Large-Scale Self- and Semi-Supervised Learning for Speech Translation</a></p>"
+
+demo = gr.Interface(fn=parse, inputs = input_,  outputs=txtbox, title=title, description=description, article = article,
+             streaming=True, interactive=True,
+             analytics_enabled=False, show_tips=False, enable_queue=True)
+demo.launch(share=True)