big change commit

.AudioDog/Include/site/python3.11/greenlet/greenlet.h

@@ -0,0 +1,164 @@
+/* -*- indent-tabs-mode: nil; tab-width: 4; -*- */
+
+/* Greenlet object interface */
+
+#ifndef Py_GREENLETOBJECT_H
+#define Py_GREENLETOBJECT_H
+
+
+#include <Python.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/* This is deprecated and undocumented. It does not change. */
+#define GREENLET_VERSION "1.0.0"
+
+#ifndef GREENLET_MODULE
+#define implementation_ptr_t void*
+#endif
+
+typedef struct _greenlet {
+    PyObject_HEAD
+    PyObject* weakreflist;
+    PyObject* dict;
+    implementation_ptr_t pimpl;
+} PyGreenlet;
+
+#define PyGreenlet_Check(op) (op && PyObject_TypeCheck(op, &PyGreenlet_Type))
+
+
+/* C API functions */
+
+/* Total number of symbols that are exported */
+#define PyGreenlet_API_pointers 12
+
+#define PyGreenlet_Type_NUM 0
+#define PyExc_GreenletError_NUM 1
+#define PyExc_GreenletExit_NUM 2
+
+#define PyGreenlet_New_NUM 3
+#define PyGreenlet_GetCurrent_NUM 4
+#define PyGreenlet_Throw_NUM 5
+#define PyGreenlet_Switch_NUM 6
+#define PyGreenlet_SetParent_NUM 7
+
+#define PyGreenlet_MAIN_NUM 8
+#define PyGreenlet_STARTED_NUM 9
+#define PyGreenlet_ACTIVE_NUM 10
+#define PyGreenlet_GET_PARENT_NUM 11
+
+#ifndef GREENLET_MODULE
+/* This section is used by modules that uses the greenlet C API */
+static void** _PyGreenlet_API = NULL;
+
+#    define PyGreenlet_Type \
+        (*(PyTypeObject*)_PyGreenlet_API[PyGreenlet_Type_NUM])
+
+#    define PyExc_GreenletError \
+        ((PyObject*)_PyGreenlet_API[PyExc_GreenletError_NUM])
+
+#    define PyExc_GreenletExit \
+        ((PyObject*)_PyGreenlet_API[PyExc_GreenletExit_NUM])
+
+/*
+ * PyGreenlet_New(PyObject *args)
+ *
+ * greenlet.greenlet(run, parent=None)
+ */
+#    define PyGreenlet_New                                        \
+        (*(PyGreenlet * (*)(PyObject * run, PyGreenlet * parent)) \
+             _PyGreenlet_API[PyGreenlet_New_NUM])
+
+/*
+ * PyGreenlet_GetCurrent(void)
+ *
+ * greenlet.getcurrent()
+ */
+#    define PyGreenlet_GetCurrent \
+        (*(PyGreenlet * (*)(void)) _PyGreenlet_API[PyGreenlet_GetCurrent_NUM])
+
+/*
+ * PyGreenlet_Throw(
+ *         PyGreenlet *greenlet,
+ *         PyObject *typ,
+ *         PyObject *val,
+ *         PyObject *tb)
+ *
+ * g.throw(...)
+ */
+#    define PyGreenlet_Throw                 \
+        (*(PyObject * (*)(PyGreenlet * self, \
+                          PyObject * typ,    \
+                          PyObject * val,    \
+                          PyObject * tb))    \
+             _PyGreenlet_API[PyGreenlet_Throw_NUM])
+
+/*
+ * PyGreenlet_Switch(PyGreenlet *greenlet, PyObject *args)
+ *
+ * g.switch(*args, **kwargs)
+ */
+#    define PyGreenlet_Switch                                              \
+        (*(PyObject *                                                      \
+           (*)(PyGreenlet * greenlet, PyObject * args, PyObject * kwargs)) \
+             _PyGreenlet_API[PyGreenlet_Switch_NUM])
+
+/*
+ * PyGreenlet_SetParent(PyObject *greenlet, PyObject *new_parent)
+ *
+ * g.parent = new_parent
+ */
+#    define PyGreenlet_SetParent                                 \
+        (*(int (*)(PyGreenlet * greenlet, PyGreenlet * nparent)) \
+             _PyGreenlet_API[PyGreenlet_SetParent_NUM])
+
+/*
+ * PyGreenlet_GetParent(PyObject* greenlet)
+ *
+ * return greenlet.parent;
+ *
+ * This could return NULL even if there is no exception active.
+ * If it does not return NULL, you are responsible for decrementing the
+ * reference count.
+ */
+#     define PyGreenlet_GetParent                                    \
+    (*(PyGreenlet* (*)(PyGreenlet*))                                 \
+     _PyGreenlet_API[PyGreenlet_GET_PARENT_NUM])
+
+/*
+ * deprecated, undocumented alias.
+ */
+#     define PyGreenlet_GET_PARENT PyGreenlet_GetParent
+
+#     define PyGreenlet_MAIN                                         \
+    (*(int (*)(PyGreenlet*))                                         \
+     _PyGreenlet_API[PyGreenlet_MAIN_NUM])
+
+#     define PyGreenlet_STARTED                                      \
+    (*(int (*)(PyGreenlet*))                                         \
+     _PyGreenlet_API[PyGreenlet_STARTED_NUM])
+
+#     define PyGreenlet_ACTIVE                                       \
+    (*(int (*)(PyGreenlet*))                                         \
+     _PyGreenlet_API[PyGreenlet_ACTIVE_NUM])
+
+
+
+
+/* Macro that imports greenlet and initializes C API */
+/* NOTE: This has actually moved to ``greenlet._greenlet._C_API``, but we
+   keep the older definition to be sure older code that might have a copy of
+   the header still works. */
+#    define PyGreenlet_Import()                                               \
+        {                                                                     \
+            _PyGreenlet_API = (void**)PyCapsule_Import("greenlet._C_API", 0); \
+        }
+
+#endif /* GREENLET_MODULE */
+
+#ifdef __cplusplus
+}
+#endif
+#endif /* !Py_GREENLETOBJECT_H */

.AudioDog/etc/jupyter/nbconfig/notebook.d/pydeck.json

@@ -0,0 +1,5 @@
+{
+    "load_extensions": {
+        "pydeck/extension": true
+    }
+}

.AudioDog/pyvenv.cfg

@@ -0,0 +1,5 @@
+home = C:\Users\rndav\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0
+include-system-site-packages = false
+version = 3.11.9
+executable = C:\Users\rndav\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\python.exe
+command = C:\Users\rndav\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\python.exe -m venv C:\Users\rndav\Documents\GitHub\AudioDog\.AudioDog

.gitattributes

@@ -1,35 +1,35 @@
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.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
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+-----END CERTIFICATE-----

README.md

@@ -1,14 +1,14 @@
----
-title: AudioDog
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.0.1
-app_file: app.py
-pinned: false
-license: mit
-short_description: testing out the nvidia parakeet model
----
-
+---
+title: AudioDog
+emoji: 💬
+colorFrom: yellow
+colorTo: purple
+sdk: gradio
+sdk_version: 5.0.1
+app_file: app.py
+pinned: false
+license: mit
+short_description: testing out the nvidia parakeet model
+---
+
 An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

app.py

@@ -1,376 +1,286 @@
-from nemo.collections.asr.models import ASRModel
-import torch
-import gradio as gr
-import spaces
-import gc
-from pathlib import Path
-from pydub import AudioSegment
-import numpy as np
-import os
-import tempfile
-import gradio.themes as gr_themes
-import csv
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-MODEL_NAME="nvidia/parakeet-tdt-0.6b-v2"
-
-model = ASRModel.from_pretrained(model_name=MODEL_NAME)
-model.eval()
-
-def get_audio_segment(audio_path, start_second, end_second):
-    """
-    Extract a segment of audio from a given audio file.
-    Parameters:
-        audio_path (str): Path to the audio file to process
-        start_second (float): Start time of the segment in seconds
-        end_second (float): End time of the segment in seconds
-    Returns:
-        tuple or None: A tuple containing (frame_rate, samples) where:
-            - frame_rate (int): The sample rate of the audio
-            - samples (numpy.ndarray): The audio samples as a numpy array
-            Returns None if there's an error processing the audio
-    """
-    if not audio_path or not Path(audio_path).exists():
-        print(f"Warning: Audio path '{audio_path}' not found or invalid for clipping.")
-        return None
-    try:
-        start_ms = int(start_second * 1000)
-        end_ms = int(end_second * 1000)
-
-        start_ms = max(0, start_ms)
-        if end_ms <= start_ms:
-            print(f"Warning: End time ({end_second}s) is not after start time ({start_second}s). Adjusting end time.")
-            end_ms = start_ms + 100
-
-        audio = AudioSegment.from_file(audio_path)
-        clipped_audio = audio[start_ms:end_ms]
-
-        samples = np.array(clipped_audio.get_array_of_samples())
-        if clipped_audio.channels == 2:
-            samples = samples.reshape((-1, 2)).mean(axis=1).astype(samples.dtype)
-
-        frame_rate = clipped_audio.frame_rate
-        if frame_rate <= 0:
-             print(f"Warning: Invalid frame rate ({frame_rate}) detected for clipped audio.")
-             frame_rate = audio.frame_rate
-
-        if samples.size == 0:
-             print(f"Warning: Clipped audio resulted in empty samples array ({start_second}s to {end_second}s).")
-             return None
-
-        return (frame_rate, samples)
-    except FileNotFoundError:
-        print(f"Error: Audio file not found at path: {audio_path}")
-        return None
-    except Exception as e:
-        print(f"Error clipping audio {audio_path} from {start_second}s to {end_second}s: {e}")
-        return None
-
-@spaces.GPU
-def get_transcripts_and_raw_times(audio_path):
-    """
-    Transcribe an audio file and generate timestamps for each segment.
-    Parameters:
-        audio_path (str): Path to the audio file to transcribe
-    Returns:
-        tuple: A tuple containing:
-            - vis_data (list): List of [start, end, text] for visualization
-            - raw_times_data (list): List of [start, end] timestamps
-            - audio_path (str): Path to the processed audio file
-            - button_update (gr.DownloadButton): Gradio button component for CSV download
-    Notes:
-        - Automatically handles audio preprocessing (resampling to 16kHz, mono conversion)
-        - Uses NVIDIA's Parakeet TDT model for transcription
-        - Generates a CSV file with transcription results
-    """
-    if not audio_path:
-        gr.Error("No audio file path provided for transcription.", duration=None)
-        # Return an update to hide the button
-        return [], [], None, gr.DownloadButton(visible=False)
-
-    vis_data = [["N/A", "N/A", "Processing failed"]]
-    raw_times_data = [[0.0, 0.0]]
-    processed_audio_path = None
-    temp_file = None
-    csv_file_path = None
-    original_path_name = Path(audio_path).name
-
-    try:
-        try:
-            gr.Info(f"Loading audio: {original_path_name}", duration=2)
-            audio = AudioSegment.from_file(audio_path)
-        except Exception as load_e:
-            gr.Error(f"Failed to load audio file {original_path_name}: {load_e}", duration=None)
-            # Return an update to hide the button
-            return [["Error", "Error", "Load failed"]], [[0.0, 0.0]], audio_path, gr.DownloadButton(visible=False)
-
-        resampled = False
-        mono = False
-
-        target_sr = 16000
-        if audio.frame_rate != target_sr:
-            try:
-                audio = audio.set_frame_rate(target_sr)
-                resampled = True
-            except Exception as resample_e:
-                 gr.Error(f"Failed to resample audio: {resample_e}", duration=None)
-                 # Return an update to hide the button
-                 return [["Error", "Error", "Resample failed"]], [[0.0, 0.0]], audio_path, gr.DownloadButton(visible=False)
-
-        if audio.channels == 2:
-            try:
-                audio = audio.set_channels(1)
-                mono = True
-            except Exception as mono_e:
-                 gr.Error(f"Failed to convert audio to mono: {mono_e}", duration=None)
-                 # Return an update to hide the button
-                 return [["Error", "Error", "Mono conversion failed"]], [[0.0, 0.0]], audio_path, gr.DownloadButton(visible=False)
-        elif audio.channels > 2:
-             gr.Error(f"Audio has {audio.channels} channels. Only mono (1) or stereo (2) supported.", duration=None)
-             # Return an update to hide the button
-             return [["Error", "Error", f"{audio.channels}-channel audio not supported"]], [[0.0, 0.0]], audio_path, gr.DownloadButton(visible=False)
-
-        if resampled or mono:
-            try:
-                temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
-                audio.export(temp_file.name, format="wav")
-                processed_audio_path = temp_file.name
-                temp_file.close()
-                transcribe_path = processed_audio_path
-                info_path_name = f"{original_path_name} (processed)"
-            except Exception as export_e:
-                gr.Error(f"Failed to export processed audio: {export_e}", duration=None)
-                if temp_file and hasattr(temp_file, 'name') and os.path.exists(temp_file.name): # Check temp_file has 'name' attribute
-                    os.remove(temp_file.name)
-                # Return an update to hide the button
-                return [["Error", "Error", "Export failed"]], [[0.0, 0.0]], audio_path, gr.DownloadButton(visible=False)
-        else:
-            transcribe_path = audio_path
-            info_path_name = original_path_name
-
-        try:
-            model.to(device)
-            gr.Info(f"Transcribing {info_path_name} on {device}...", duration=2)
-            output = model.transcribe([transcribe_path], timestamps=True)
-
-            if not output or not isinstance(output, list) or not output[0] or not hasattr(output[0], 'timestamp') or not output[0].timestamp or 'segment' not in output[0].timestamp:
-                 gr.Error("Transcription failed or produced unexpected output format.", duration=None)
-                 # Return an update to hide the button
-                 return [["Error", "Error", "Transcription Format Issue"]], [[0.0, 0.0]], audio_path, gr.DownloadButton(visible=False)
-
-            segment_timestamps = output[0].timestamp['segment']
-            csv_headers = ["Start (s)", "End (s)", "Segment"]
-            vis_data = [[f"{ts['start']:.2f}", f"{ts['end']:.2f}", ts['segment']] for ts in segment_timestamps]
-            raw_times_data = [[ts['start'], ts['end']] for ts in segment_timestamps]
-
-            # Default button update (hidden) in case CSV writing fails
-            button_update = gr.DownloadButton(visible=False)
-            try:
-                temp_csv_file = tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode='w', newline='', encoding='utf-8')
-                writer = csv.writer(temp_csv_file)
-                writer.writerow(csv_headers)
-                writer.writerows(vis_data)
-                csv_file_path = temp_csv_file.name
-                temp_csv_file.close()
-                print(f"CSV transcript saved to temporary file: {csv_file_path}")
-                # If CSV is saved, create update to show button with path
-                button_update = gr.DownloadButton(value=csv_file_path, visible=True)
-            except Exception as csv_e:
-                gr.Error(f"Failed to create transcript CSV file: {csv_e}", duration=None)
-                print(f"Error writing CSV: {csv_e}")
-                # csv_file_path remains None, button_update remains hidden
-
-            gr.Info("Transcription complete.", duration=2)
-            # Return the data and the button update dictionary
-            return vis_data, raw_times_data, audio_path, button_update
-
-        except torch.cuda.OutOfMemoryError as e:
-            error_msg = 'CUDA out of memory. Please try a shorter audio or reduce GPU load.'
-            print(f"CUDA OutOfMemoryError: {e}")
-            gr.Error(error_msg, duration=None)
-            # Return an update to hide the button
-            return [["OOM", "OOM", error_msg]], [[0.0, 0.0]], audio_path, gr.DownloadButton(visible=False)
-
-        except FileNotFoundError:
-            error_msg = f"Audio file for transcription not found: {Path(transcribe_path).name}."
-            print(f"Error: Transcribe audio file not found at path: {transcribe_path}")
-            gr.Error(error_msg, duration=None)
-            # Return an update to hide the button
-            return [["Error", "Error", "File not found for transcription"]], [[0.0, 0.0]], audio_path, gr.DownloadButton(visible=False)
-
-        except Exception as e:
-            error_msg = f"Transcription failed: {e}"
-            print(f"Error during transcription processing: {e}")
-            gr.Error(error_msg, duration=None)
-            vis_data = [["Error", "Error", error_msg]]
-            raw_times_data = [[0.0, 0.0]]
-            # Return an update to hide the button
-            return vis_data, raw_times_data, audio_path, gr.DownloadButton(visible=False)
-        finally:
-            try:
-                if 'model' in locals() and hasattr(model, 'cpu'):
-                     if device == 'cuda':
-                          model.cpu()
-                gc.collect()
-                if device == 'cuda':
-                    torch.cuda.empty_cache()
-            except Exception as cleanup_e:
-                print(f"Error during model cleanup: {cleanup_e}")
-                gr.Warning(f"Issue during model cleanup: {cleanup_e}", duration=5)
-
-    finally:
-        if processed_audio_path and os.path.exists(processed_audio_path):
-            try:
-                os.remove(processed_audio_path)
-                print(f"Temporary audio file {processed_audio_path} removed.")
-            except Exception as e:
-                print(f"Error removing temporary audio file {processed_audio_path}: {e}")
-
-def play_segment(evt: gr.SelectData, raw_ts_list, current_audio_path):
-    """
-    Play a selected segment from the transcription results.
-    Parameters:
-        evt (gr.SelectData): Gradio select event containing the index of selected segment
-        raw_ts_list (list): List of [start, end] timestamps for all segments
-        current_audio_path (str): Path to the current audio file being processed
-    Returns:
-        gr.Audio: Gradio Audio component containing the selected segment for playback
-    Notes:
-        - Extracts and plays the audio segment corresponding to the selected transcription
-        - Returns None if segment extraction fails or inputs are invalid
-    """
-    if not isinstance(raw_ts_list, list):
-        print(f"Warning: raw_ts_list is not a list ({type(raw_ts_list)}). Cannot play segment.")
-        return gr.Audio(value=None, label="Selected Segment")
-
-    if not current_audio_path:
-        print("No audio path available to play segment from.")
-        return gr.Audio(value=None, label="Selected Segment")
-
-    selected_index = evt.index[0]
-
-    if selected_index < 0 or selected_index >= len(raw_ts_list):
-         print(f"Invalid index {selected_index} selected for list of length {len(raw_ts_list)}.")
-         return gr.Audio(value=None, label="Selected Segment")
-
-    if not isinstance(raw_ts_list[selected_index], (list, tuple)) or len(raw_ts_list[selected_index]) != 2:
-         print(f"Warning: Data at index {selected_index} is not in the expected format [start, end].")
-         return gr.Audio(value=None, label="Selected Segment")
-
-    start_time_s, end_time_s = raw_ts_list[selected_index]
-
-    print(f"Attempting to play segment: {current_audio_path} from {start_time_s:.2f}s to {end_time_s:.2f}s")
-
-    segment_data = get_audio_segment(current_audio_path, start_time_s, end_time_s)
-
-    if segment_data:
-        print("Segment data retrieved successfully.")
-        return gr.Audio(value=segment_data, autoplay=True, label=f"Segment: {start_time_s:.2f}s - {end_time_s:.2f}s", interactive=False)
-    else:
-        print("Failed to get audio segment data.")
-        return gr.Audio(value=None, label="Selected Segment")
-
-article = (
-    "<p style='font-size: 1.1em;'>"
-    "This demo showcases <code><a href='https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2'>parakeet-tdt-0.6b-v2</a></code>, a 600-million-parameter model designed for high-quality English speech recognition."
-    "</p>"
-    "<p><strong style='color: red; font-size: 1.2em;'>Key Features:</strong></p>"
-    "<ul style='font-size: 1.1em;'>"
-    "    <li>Automatic punctuation and capitalization</li>"
-    "    <li>Accurate word-level timestamps (click on a segment in the table below to play it!)</li>"
-    "    <li>Efficiently transcribes long audio segments (up to 20 minutes) <small>(For even longer audios, see <a href='https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_chunked_inference/rnnt/speech_to_text_buffered_infer_rnnt.py' target='_blank'>this script</a>)</small></li>"
-    "    <li>Robust performance on spoken numbers, and song lyrics transcription </li>"
-    "</ul>"
-    "<p style='font-size: 1.1em;'>"
-    "This model is <strong>available for commercial and non-commercial use</strong>."
-    "</p>"
-    "<p style='text-align: center;'>"
-    "<a href='https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2' target='_blank'>🎙️ Learn more about the Model</a> | "
-    "<a href='https://arxiv.org/abs/2305.05084' target='_blank'>📄 Fast Conformer paper</a> | "
-    "<a href='https://arxiv.org/abs/2304.06795' target='_blank'>📚 TDT paper</a> | "
-    "<a href='https://github.com/NVIDIA/NeMo' target='_blank'>🧑‍💻 NeMo Repository</a>"
-    "</p>"
-)
-
-examples = [
-    ["data/example-yt_saTD1u8PorI.mp3"],
-]
-
-# Define an NVIDIA-inspired theme
-nvidia_theme = gr_themes.Default(
-    primary_hue=gr_themes.Color(
-        c50="#E6F1D9", # Lightest green
-        c100="#CEE3B3",
-        c200="#B5D58C",
-        c300="#9CC766",
-        c400="#84B940",
-        c500="#76B900", # NVIDIA Green
-        c600="#68A600",
-        c700="#5A9200",
-        c800="#4C7E00",
-        c900="#3E6A00", # Darkest green
-        c950="#2F5600"
-    ),
-    neutral_hue="gray", # Use gray for neutral elements
-    font=[gr_themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui", "sans-serif"],
-).set()
-
-# Apply the custom theme
-with gr.Blocks(theme=nvidia_theme) as demo:
-    model_display_name = MODEL_NAME.split('/')[-1] if '/' in MODEL_NAME else MODEL_NAME
-    gr.Markdown(f"<h1 style='text-align: center; margin: 0 auto;'>Speech Transcription with {model_display_name}</h1>")
-    gr.HTML(article)
-
-    current_audio_path_state = gr.State(None)
-    raw_timestamps_list_state = gr.State([])
-
-    with gr.Tabs():
-        with gr.TabItem("Audio File"):
-            file_input = gr.Audio(sources=["upload"], type="filepath", label="Upload Audio File")
-            gr.Examples(examples=examples, inputs=[file_input], label="Example Audio Files (Click to Load)")
-            file_transcribe_btn = gr.Button("Transcribe Uploaded File", variant="primary")
-        
-        with gr.TabItem("Microphone"):
-            mic_input = gr.Audio(sources=["microphone"], type="filepath", label="Record Audio")
-            mic_transcribe_btn = gr.Button("Transcribe Microphone Input", variant="primary")
-
-    gr.Markdown("---")
-    gr.Markdown("<p><strong style='color: #FF0000; font-size: 1.2em;'>Transcription Results (Click row to play segment)</strong></p>")
-
-    # Define the DownloadButton *before* the DataFrame
-    download_btn = gr.DownloadButton(label="Download Transcript (CSV)", visible=False)
-
-    vis_timestamps_df = gr.DataFrame(
-        headers=["Start (s)", "End (s)", "Segment"],
-        datatype=["number", "number", "str"],
-        wrap=True,
-        label="Transcription Segments"
-    )
-
-    # selected_segment_player was defined after download_btn previously, keep it after df for layout
-    selected_segment_player = gr.Audio(label="Selected Segment", interactive=False)
-
-    mic_transcribe_btn.click(
-        fn=get_transcripts_and_raw_times,
-        inputs=[mic_input],
-        outputs=[vis_timestamps_df, raw_timestamps_list_state, current_audio_path_state, download_btn],
-        api_name="transcribe_mic"
-    )
-
-    file_transcribe_btn.click(
-        fn=get_transcripts_and_raw_times,
-        inputs=[file_input],
-        outputs=[vis_timestamps_df, raw_timestamps_list_state, current_audio_path_state, download_btn],
-        api_name="transcribe_file"
-    )
-
-    vis_timestamps_df.select(
-        fn=play_segment,
-        inputs=[raw_timestamps_list_state, current_audio_path_state],
-        outputs=[selected_segment_player],
-    )
-
-if __name__ == "__main__":
-    print("Launching Gradio Demo...")
-    demo.queue()
+from nemo.collections.asr.models import ASRModel
+import torch
+import gradio as gr
+import spaces
+import gc
+from pathlib import Path
+from pydub import AudioSegment
+import numpy as np
+import os
+import tempfile
+import gradio.themes as gr_themes
+import csv
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+MODEL_NAME="nvidia/parakeet-tdt-0.6b-v2"
+
+model = ASRModel.from_pretrained(model_name=MODEL_NAME)
+model.eval()
+
+def get_audio_segment(audio_path, start_second, end_second):
+    """
+    Extract a segment of audio from a given audio file.
+    Parameters:
+        audio_path (str): Path to the audio file to process
+        start_second (float): Start time of the segment in seconds
+        end_second (float): End time of the segment in seconds
+    Returns:
+        tuple or None: A tuple containing (frame_rate, samples) where:
+            - frame_rate (int): The sample rate of the audio
+            - samples (numpy.ndarray): The audio samples as a numpy array
+            Returns None if there's an error processing the audio
+    """
+    if not audio_path or not Path(audio_path).exists():
+        print(f"Warning: Audio path '{audio_path}' not found or invalid for clipping.")
+        return None
+    try:
+        start_ms = int(start_second * 1000)
+        end_ms = int(end_second * 1000)
+
+        start_ms = max(0, start_ms)
+        if end_ms <= start_ms:
+            print(f"Warning: End time ({end_second}s) is not after start time ({start_second}s). Adjusting end time.")
+            end_ms = start_ms + 100
+
+        audio = AudioSegment.from_file(audio_path)
+        clipped_audio = audio[start_ms:end_ms]
+
+        samples = np.array(clipped_audio.get_array_of_samples())
+        if clipped_audio.channels == 2:
+            samples = samples.reshape((-1, 2)).mean(axis=1).astype(samples.dtype)
+
+        frame_rate = clipped_audio.frame_rate
+        if frame_rate <= 0:
+             print(f"Warning: Invalid frame rate ({frame_rate}) detected for clipped audio.")
+             frame_rate = audio.frame_rate
+
+        if samples.size == 0:
+             print(f"Warning: Clipped audio resulted in empty samples array ({start_second}s to {end_second}s).")
+             return None
+
+        return (frame_rate, samples)
+    except FileNotFoundError:
+        print(f"Error: Audio file not found at path: {audio_path}")
+        return None
+    except Exception as e:
+        print(f"Error clipping audio {audio_path} from {start_second}s to {end_second}s: {e}")
+        return None
+
+@spaces.GPU
+@spaces.GPU
+def get_transcripts_and_raw_times(audio_path):
+    if not audio_path:
+        gr.Error("No audio file path provided for transcription.", duration=None)
+        return [], [], None, gr.DownloadButton(visible=False)
+
+    original_path_name = Path(audio_path).name
+    try:
+        gr.Info(f"Loading audio: {original_path_name}", duration=2)
+        full_audio = AudioSegment.from_file(audio_path)
+    except Exception as load_e:
+        gr.Error(f"Failed to load audio file {original_path_name}: {load_e}", duration=None)
+        return [["Error", "Error", "Load failed"]], [[0.0, 0.0]], audio_path, gr.DownloadButton(visible=False)
+
+    # Ensure 16kHz mono
+    if full_audio.frame_rate != 16000:
+        full_audio = full_audio.set_frame_rate(16000)
+    if full_audio.channels != 1:
+        full_audio = full_audio.set_channels(1)
+
+    chunk_duration_ms = 5 * 60 * 1000  # 5 minutes in milliseconds
+    total_duration_ms = len(full_audio)
+    total_chunks = (total_duration_ms + chunk_duration_ms - 1) // chunk_duration_ms
+
+    vis_data = []
+    raw_times_data = []
+
+    model.to(device)
+
+    for i, start_ms in enumerate(range(0, total_duration_ms, chunk_duration_ms), start=1):
+        end_ms = min(start_ms + chunk_duration_ms, total_duration_ms)
+        chunk = full_audio[start_ms:end_ms]
+
+        gr.Info(f"Transcribing chunk {i} of {total_chunks} ({start_ms/1000:.0f}s to {end_ms/1000:.0f}s)...", duration=3)
+
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_wav:
+            chunk.export(temp_wav.name, format="wav")
+            temp_wav_path = temp_wav.name
+
+        try:
+            output = model.transcribe([temp_wav_path], timestamps=True)
+            if not output or not output[0].timestamp or 'segment' not in output[0].timestamp:
+                continue
+
+            for ts in output[0].timestamp['segment']:
+                abs_start = ts['start'] + (start_ms / 1000.0)
+                abs_end = ts['end'] + (start_ms / 1000.0)
+                vis_data.append([f"{abs_start:.2f}", f"{abs_end:.2f}", ts['segment']])
+                raw_times_data.append([abs_start, abs_end])
+        except Exception as e:
+            gr.Warning(f"Chunk {i} failed: {e}", duration=3)
+        finally:
+            os.remove(temp_wav_path)
+
+    model.cpu()
+    gc.collect()
+    if device == "cuda":
+        torch.cuda.empty_cache()
+
+    # Generate CSV
+    button_update = gr.DownloadButton(visible=False)
+    try:
+        csv_headers = ["Start (s)", "End (s)", "Segment"]
+        temp_csv_file = tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode='w', newline='', encoding='utf-8')
+        writer = csv.writer(temp_csv_file)
+        writer.writerow(csv_headers)
+        writer.writerows(vis_data)
+        csv_file_path = temp_csv_file.name
+        temp_csv_file.close()
+        button_update = gr.DownloadButton(value=csv_file_path, visible=True)
+    except Exception as csv_e:
+        gr.Error(f"Failed to create transcript CSV file: {csv_e}", duration=None)
+
+    gr.Info("Transcription complete.", duration=2)
+    return vis_data, raw_times_data, audio_path, button_update
+
+@spaces.GPU
+def play_segment(evt: gr.SelectData, raw_ts_list, current_audio_path):
+    """
+    Play a selected segment from the transcription results.
+    Parameters:
+        evt (gr.SelectData): Gradio select event containing the index of selected segment
+        raw_ts_list (list): List of [start, end] timestamps for all segments
+        current_audio_path (str): Path to the current audio file being processed
+    Returns:
+        gr.Audio: Gradio Audio component containing the selected segment for playback
+    Notes:
+        - Extracts and plays the audio segment corresponding to the selected transcription
+        - Returns None if segment extraction fails or inputs are invalid
+    """
+    if not isinstance(raw_ts_list, list):
+        print(f"Warning: raw_ts_list is not a list ({type(raw_ts_list)}). Cannot play segment.")
+        return gr.Audio(value=None, label="Selected Segment")
+
+    if not current_audio_path:
+        print("No audio path available to play segment from.")
+        return gr.Audio(value=None, label="Selected Segment")
+
+    selected_index = evt.index[0]
+
+    if selected_index < 0 or selected_index >= len(raw_ts_list):
+         print(f"Invalid index {selected_index} selected for list of length {len(raw_ts_list)}.")
+         return gr.Audio(value=None, label="Selected Segment")
+
+    if not isinstance(raw_ts_list[selected_index], (list, tuple)) or len(raw_ts_list[selected_index]) != 2:
+         print(f"Warning: Data at index {selected_index} is not in the expected format [start, end].")
+         return gr.Audio(value=None, label="Selected Segment")
+
+    start_time_s, end_time_s = raw_ts_list[selected_index]
+
+    print(f"Attempting to play segment: {current_audio_path} from {start_time_s:.2f}s to {end_time_s:.2f}s")
+
+    segment_data = get_audio_segment(current_audio_path, start_time_s, end_time_s)
+
+    if segment_data:
+        print("Segment data retrieved successfully.")
+        return gr.Audio(value=segment_data, autoplay=True, label=f"Segment: {start_time_s:.2f}s - {end_time_s:.2f}s", interactive=False)
+    else:
+        print("Failed to get audio segment data.")
+        return gr.Audio(value=None, label="Selected Segment")
+
+article = (
+    "<p style='font-size: 1.1em;'>"
+    "AudioDog uses <code><a href='https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2'>parakeet-tdt-0.6b-v2</a></code>, a 600-million-parameter model designed for high-quality English speech recognition."
+    "</p>"
+    "<p><strong style='color: red; font-size: 1.2em;'>Key Features:</strong></p>"
+    "<ul style='font-size: 1.1em;'>"
+    "    <li>Automatic punctuation and capitalization</li>"
+    "    <li>Accurate word-level timestamps (click on a segment in the table below to play it!)</li>"
+    "    <li>Efficiently transcribes long audio segments by chunking them into smaller segments and stitching them together when done.</li>"
+    "    <li>MP3 support for audio input and output, works well on downloaded YouTube videos.</li>"
+    "</ul>"
+)
+examples = [
+    ["data/example-yt_saTD1u8PorI.mp3"],
+]
+
+
+# Define an NVIDIA-inspired theme
+nvidia_theme = gr_themes.Default(
+    primary_hue=gr_themes.Color(
+        c50="#E6F1D9", # Lightest green
+        c100="#CEE3B3",
+        c200="#B5D58C",
+        c300="#9CC766",
+        c400="#84B940",
+        c500="#76B900", # NVIDIA Green
+        c600="#68A600",
+        c700="#5A9200",
+        c800="#4C7E00",
+        c900="#3E6A00", # Darkest green
+        c950="#2F5600"
+    ),
+    neutral_hue="gray", # Use gray for neutral elements
+    font=[gr_themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui", "sans-serif"],
+).set()
+
+# Apply the custom theme
+with gr.Blocks(theme=nvidia_theme) as demo:
+    gr.Image("pics/AD.jpg", label="AudioDog Logo", show_label=False)
+    model_display_name = MODEL_NAME.split('/')[-1] if '/' in MODEL_NAME else MODEL_NAME
+    gr.Markdown(f"<h1 style='text-align: center; margin: 0 auto;'>AudioDog, powered by {model_display_name}</h1>")
+    gr.HTML(article)
+
+    current_audio_path_state = gr.State(None)
+    raw_timestamps_list_state = gr.State([])
+
+    with gr.Tabs():
+        with gr.TabItem("Audio File"):
+            file_input = gr.Audio(sources=["upload"], type="filepath", label="Upload Audio File")
+            gr.Examples(examples=examples, inputs=[file_input], label="Example Audio Files (Click to Load)")
+            file_transcribe_btn = gr.Button("Transcribe Uploaded File", variant="primary")
+        
+        with gr.TabItem("Microphone"):
+            mic_input = gr.Audio(sources=["microphone"], type="filepath", label="Record Audio")
+            mic_transcribe_btn = gr.Button("Transcribe Microphone Input", variant="primary")
+
+    gr.Markdown("---")
+    gr.Markdown("<p><strong style='color: #FF0000; font-size: 1.2em;'>Transcription Results (Click row to play segment)</strong></p>")
+
+    # Define the DownloadButton *before* the DataFrame
+    download_btn = gr.DownloadButton(label="Download Transcript (CSV)", visible=False)
+
+    vis_timestamps_df = gr.DataFrame(
+        headers=["Start (s)", "End (s)", "Segment"],
+        datatype=["number", "number", "str"],
+        wrap=True,
+        label="Transcription Segments"
+    )
+
+    # selected_segment_player was defined after download_btn previously, keep it after df for layout
+    selected_segment_player = gr.Audio(label="Selected Segment", interactive=False)
+
+    mic_transcribe_btn.click(
+        fn=get_transcripts_and_raw_times,
+        inputs=[mic_input],
+        outputs=[vis_timestamps_df, raw_timestamps_list_state, current_audio_path_state, download_btn],
+        api_name="transcribe_mic"
+    )
+
+    file_transcribe_btn.click(
+        fn=get_transcripts_and_raw_times,
+        inputs=[file_input],
+        outputs=[vis_timestamps_df, raw_timestamps_list_state, current_audio_path_state, download_btn],
+        api_name="transcribe_file"
+    )
+
+    vis_timestamps_df.select(
+        fn=play_segment,
+        inputs=[raw_timestamps_list_state, current_audio_path_state],
+        outputs=[selected_segment_player],
+    )
+
+if __name__ == "__main__":
+    print("Launching AudioDog...")
+    demo.queue()
     demo.launch()

qa.py

@@ -0,0 +1,8 @@
+import torch, platform
+print("torch:", torch.__version__)
+print("torch compiled CUDA:", torch.version.cuda)
+print("CUDA available:", torch.cuda.is_available())
+if torch.cuda.is_available():
+    print("GPU:", torch.cuda.get_device_name(0))
+    print("Device capability (SM):", torch.cuda.get_device_capability(0))  # e.g., (9, 0)
+    print("Torch arch list:", torch.cuda.get_arch_list())                  # what the wheel contains

requirements.txt

@@ -1,5 +1,3 @@
-Cython
-git+https://github.com/NVIDIA/NeMo.git@r2.3.0#egg=nemo_toolkit[asr]
-numpy<2.0
-cuda-python>=12.3
-gradio>=5.39.0
+Cython
+git+https://github.com/NVIDIA/NeMo.git@r2.3.0#egg=nemo_toolkit[asr]
+numpy<2.0