First version

.gitignore

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+.conda
+__pycache__
+*.pyc
+.vscode
+.DS_Store

README.md

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 ---
 title: GrAImmarian
-emoji: 🐠
+emoji: 🗣️
 colorFrom: purple
 colorTo: blue
 sdk: gradio
 sdk_version: 5.3.0
 app_file: app.py
-pinned: false
+pinned: true
 short_description: Utilizes ASR to check for filler words when public speaking
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from transformers import pipeline
+import numpy as np
+
+transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
+
+
+def transcribe(state, words_list, new_chunk):
+    print(f"state: {state}")
+
+    if state is None:
+        state = {}
+
+    stream = state.get("stream", None)
+    previous_transcription = state.get("full_transcription", "")
+    previous_counts_of_words = state.get("counts_of_words", {})
+
+    if new_chunk is None:
+        gr.Info("You can start transcribing by clicking on the Record button")
+        print("new chunk is None")
+        return state, previous_counts_of_words, previous_transcription
+
+    sr, y = new_chunk
+
+    try:
+        words_to_check_for = [word.strip() for word in words_list.split(",")]
+    except:
+        gr.Warning("Please enter a valid list of words to check for")
+        words_to_check_for = []
+
+    # Convert to mono if stereo
+    if y.ndim > 1:
+        y = y.mean(axis=1)
+
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
+
+    if stream is not None:
+        stream = np.concatenate([stream, y])
+    else:
+        stream = y
+
+    try:
+        new_transcription = transcriber({"sampling_rate": sr, "raw": stream})
+    except Exception as e:
+        gr.Error(f"Transcription failed. Error: {e}")
+        print(f"Transcription failed. Error: {e}")
+        return state, previous_counts_of_words, previous_transcription
+
+    print(f"new transcription: {new_transcription}")
+    new_transcription_text = new_transcription["text"]
+    full_transcription_text = f"{previous_transcription} {new_transcription_text}"
+
+    new_transcription_text_lower = new_transcription_text.lower()
+
+    new_counts_of_words = {
+        word: new_transcription_text_lower.count(word) for word in words_to_check_for
+    }
+
+    new_counts_of_words = {
+        word: new_counts_of_words.get(word, 0) + previous_counts_of_words.get(word, 0)
+        for word in words_to_check_for
+    }
+
+    new_state = {
+        "stream": stream,
+        "full_transcription": full_transcription_text,
+        "counts_of_words": new_counts_of_words,
+    }
+
+    print(f"new state: {new_state}")
+
+    return new_state, new_counts_of_words, full_transcription_text
+
+
+demo = gr.Interface(
+    transcribe,
+    [
+        "state",
+        gr.Textbox(label="List of filer words"),
+        gr.Audio(sources=["microphone"], streaming=True),
+    ],
+    ["state", gr.JSON(label="Filler words count"), gr.Text(label="Transcription")],
+    live=True,
+)
+
+demo.launch()

requirements.txt

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+gradio==5.3.0
+transformers==4.46.0
+torchaudio==2.5.0