app.py

app.py

@@ -0,0 +1,72 @@
+git clone https://huggingface.co/spaces/aditii09/hindi-asr
+
+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_with_lm(logits):
+    result = processor_with_LM.batch_decode(logits.cpu().numpy())
+    text = result.text
+    transcription = text[0].replace('<s>','')
+    return transcription
+
+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, applyLM):
+    input_values = read_file_and_process(wav_file)
+    with torch.no_grad():
+        logits = model(**input_values).logits
+    if applyLM:
+        return parse_transcription_with_lm(wav_file)
+    else:
+        return parse_transcription(wav_file)
+   
+
+    if applyLM:
+        return parse_transcription_with_lm(logits)
+    else:
+        return parse_transcription(logits)
+
+    
+model_id = "aditii09/hindi-asr"
+processor = Wav2Vec2Processor.from_pretrained(model_id)
+processor_with_LM = Wav2Vec2ProcessorWithLM.from_pretrained(model_id)
+model = Wav2Vec2ForCTC.from_pretrained(model_id)
+
+    
+input_ = gr.Audio(source="microphone", type="filepath") 
+txtbox = gr.Textbox(
+            label="Output from model will appear here:",
+            lines=5
+        )
+chkbox = gr.Checkbox(label="Apply LM", value=False)
+
+
+gr.Interface(parse, inputs = [input_, chkbox],  outputs=txtbox,
+             streaming=True, interactive=True,
+             analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False);