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app.py

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+import soundfile as sf
+import torch
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor,Wav2Vec2ProcessorWithLM
+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,processor):
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
+    return transcription
+
+
+def parse(wav_file, language):
+
+    if language == 'Hindi':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
+    elif language == 'Odia':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-odia-orm-100")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-odia-orm-100")
+    elif language == 'Assamese':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-assamese-asm-8")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-assamese-asm-8")
+    elif language == 'Sanskrit':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-sanskrit-sam-60")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-sanskrit-sam-60")
+    elif language == 'Punjabi':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10")
+    elif language == 'Urdu':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-urdu-urm-60")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-urdu-urm-60")
+    elif language == 'Rajasthani':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-rajasthani-raj-45")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-rajasthani-raj-45")
+    elif language == 'Marathi':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-marathi-mrm-100")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-marathi-mrm-100")
+    elif language == 'Malayalam':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-malayalam-mlm-8")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-malayalam-mlm-8")
+    elif language == 'Maithili':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-maithili-maim-50")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-maithili-maim-50")
+    elif language == 'Dogri':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-dogri-doi-55")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-dogri-doi-55")
+    elif language == 'Bhojpuri':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-bhojpuri-bhom-60")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-bhojpuri-bhom-60")
+    elif language == 'Tamil':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-tamil-tam-250")
+    elif language == 'Telugu':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-telugu-tem-100")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-telugu-tem-100")
+    elif language == 'Nepali':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-nepali-nem-130")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-nepali-nem-130")
+    elif language == 'Kannada':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-kannada-knm-560")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-kannada-knm-560")
+    elif language == 'Gujarati':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-gujarati-gnm-100")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-gujarati-gnm-100")
+    elif language == 'Bengali':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200")
+    elif language == 'English':
+        processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700")
+        model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-indian-english-enm-700")
+    
+
+    input_values = read_file_and_process(wav_file)
+    with torch.no_grad():
+        logits = model(**input_values).logits
+
+    return parse_transcription(logits, processor)
+
+
+options = ['Hindi','Odia','Assamese','Sanskrit','Punjabi','Urdu','Rajasthani','Marathi','Malayalam','Maithili','Dogri','Bhojpuri','Tamil','Telugu','Nepali','Kannada','Gujarati','Bengali','English']
+
+
+language = gr.Dropdown(options,label="Select language",value = "Hindi")  
+input_ = gr.Audio(source="upload", type="filepath")  
+txtbox = gr.Textbox(
+            label="Output from model will appear here:",
+            lines=5
+        )
+
+
+gr.Interface(parse, inputs = [input_,language ],  outputs=txtbox,
+             streaming=True, interactive=True,
+             analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False);

packages.txt

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+libsndfile1
+sox
+ffmpeg

requirements.txt

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+gradio
+https://github.com/kpu/kenlm/archive/master.zip
+pyctcdecode
+soundfile
+torch
+transformers
+sox
+scipy