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README.md

@@ -1,13 +1,33 @@
 ---
-title: Hindi Audio Text
-emoji: 🌍
-colorFrom: yellow
-colorTo: pink
+title: Vakyansh Hindi Speech Recognition Demo (Wav2vec2) 
+emoji: 🐠
+colorFrom: indigo
+colorTo: gray
 sdk: gradio
-sdk_version: 3.39.0
 app_file: app.py
 pinned: false
-license: openrail
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Configuration
+
+`title`: _string_  
+Display title for the Space
+
+`emoji`: _string_  
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_  
+Can be either `gradio` or `streamlit`
+
+`app_file`: _string_  
+Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_  
+Whether the Space stays on top of your list.

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
+
+#https://github.com/kpu/kenlm/archive/master.zip
+
+
+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(logits)
+    else:
+        return parse_transcription(logits)
+
+    
+model_id = "Harveenchadha/vakyansh-wav2vec2-hindi-him-4200"
+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);

packages.txt

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+libsndfile1
+sox
+ffmpeg

requirements.txt

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+gradio
+pyctcdecode
+soundfile
+torch
+transformers
+sox
+scipy
+