Files changed (1) hide show
  1. app.py +64 -3
app.py CHANGED
@@ -1,4 +1,65 @@
 
 
 
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  import gradio as gr
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- name_list = ['models/Harveenchadha/hindi_model_with_lm_vakyansh']
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- interfaces = [gr.Interface.load(name) for name in name_list]
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- gr.mix.Parallel(*interfaces, title="Hindi Wav2Vec2 model with LM head", description="Wav2Vec with LM head for Hindi").launch()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import soundfile as sf
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+ import torch
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+ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor,Wav2Vec2ProcessorWithLM
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  import gradio as gr
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+ import sox
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+ import subprocess
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+
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+
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+ def read_file_and_process(wav_file):
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+ filename = wav_file.split('.')[0]
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+ filename_16k = filename + "16k.wav"
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+ resampler(wav_file, filename_16k)
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+ speech, _ = sf.read(filename_16k)
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+ inputs = processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True)
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+
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+ return inputs
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+
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+
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+ def resampler(input_file_path, output_file_path):
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+ command = (
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+ f"ffmpeg -hide_banner -loglevel panic -i {input_file_path} -ar 16000 -ac 1 -bits_per_raw_sample 16 -vn "
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+ f"{output_file_path}"
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+ )
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+ subprocess.call(command, shell=True)
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+
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+
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+ def parse_transcription_with_lm(logits):
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+ result = processor_with_LM.batch_decode(logits.cpu().numpy())
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+ text = result.text
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+ transcription = text[0].replace('<s>','')
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+ return transcription
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+
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+ def parse_transcription(logits):
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+ predicted_ids = torch.argmax(logits, dim=-1)
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+ transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
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+ return transcription
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+
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+ def parse(wav_file, applyLM):
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+ input_values = read_file_and_process(wav_file)
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+ with torch.no_grad():
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+ logits = model(**input_values).logits
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+
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+ if applyLM:
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+ return parse_transcription_with_lm(logits)
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+ else:
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+ return parse_transcription(logits)
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+
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+ model_id = "Harveenchadha/hindi_model_with_lm_vakyansh"
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+
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+ processor = Wav2Vec2Processor.from_pretrained(model_id)
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+ processor_with_LM = Wav2Vec2ProcessorWithLM.from_pretrained(model_id)
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+ model = Wav2Vec2ForCTC.from_pretrained(model_id)
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+
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+
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+ input_ = gr.Audio(source="microphone", type="filepath")
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+ txtbox = gr.Textbox(
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+ label="Output from model will appear here:",
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+ lines=5
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+ )
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+ chkbox = gr.Checkbox(label="Apply LM", value=False)
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+
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+
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+ gr.Interface(parse, inputs = [input_, chkbox], outputs=txtbox,
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+ streaming=True, interactive=True,
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+ analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False);