whisper-in-colab-batch-microphe-mp3-conversion-with-gradio.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"QK7x97iZXiab"},"source":["## Mount drive"]},{"cell_type":"code","metadata":{"id":"ybMmQNlYF_hY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683480478390,"user_tz":-120,"elapsed":30811,"user":{"displayName":"Lisa Ming","userId":"02034015347897270913"}},"outputId":"c1ef258c-8d00-4a0b-8120-46241188d620"},"source":["#Mount Google Drive as folder\n","from google.colab import drive\n","drive.mount('/content/drive', force_remount=True)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"markdown","source":["## Libraries"],"metadata":{"id":"pdZ6dXZdV81_"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZsJUxc0aRsAf","outputId":"b97aac70-dac5-495d-9a30-9322469c67e5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683480512796,"user_tz":-120,"elapsed":34411,"user":{"displayName":"Lisa Ming","userId":"02034015347897270913"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n","  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n","  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m55.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h  Building wheel for openai-whisper (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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\u001b[?25l\u001b[?25hdone\n"]}],"source":["! pip install git+https://github.com/openai/whisper.git -q\n","! pip install gradio -q"]},{"cell_type":"code","source":["import shutil\n","import whisper\n","import gradio as gr \n","import time\n","\n","model = whisper.load_model(\"base\")\n"],"metadata":{"id":"Kr5faKybKi4p","outputId":"54250d84-1282-4961-ee14-1b81009083bf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683480531480,"user_tz":-120,"elapsed":18708,"user":{"displayName":"Lisa Ming","userId":"02034015347897270913"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["100%|███████████████████████████████████████| 139M/139M [00:02<00:00, 59.0MiB/s]\n"]}]},{"cell_type":"code","source":["model.device"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"u_6_s2iHboR4","outputId":"b8236c83-c459-425f-82c0-459b1e2301ca","executionInfo":{"status":"ok","timestamp":1683480531482,"user_tz":-120,"elapsed":23,"user":{"displayName":"Lisa Ming","userId":"02034015347897270913"}}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["device(type='cuda', index=0)"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","source":["!pip show gradio"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vI2oZ_h8TjGt","executionInfo":{"status":"ok","timestamp":1683480536118,"user_tz":-120,"elapsed":4649,"user":{"displayName":"Lisa Ming","userId":"02034015347897270913"}},"outputId":"33faa1d1-1317-4e2e-e43f-7f0b818d2021"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Name: gradio\n","Version: 3.28.3\n","Summary: Python library for easily interacting with trained machine learning models\n","Home-page: \n","Author: \n","Author-email: Abubakar Abid <team@gradio.app>, Ali Abid <team@gradio.app>, Ali Abdalla <team@gradio.app>, Dawood Khan <team@gradio.app>, Ahsen Khaliq <team@gradio.app>, Pete Allen <team@gradio.app>, Ömer Faruk Özdemir <team@gradio.app>\n","License: \n","Location: /usr/local/lib/python3.10/dist-packages\n","Requires: aiofiles, aiohttp, altair, fastapi, ffmpy, gradio-client, httpx, huggingface-hub, jinja2, markdown-it-py, markupsafe, matplotlib, mdit-py-plugins, numpy, orjson, pandas, pillow, pydantic, pydub, pygments, python-multipart, pyyaml, requests, semantic-version, typing-extensions, uvicorn, websockets\n","Required-by: \n"]}]},{"cell_type":"code","source":["# from IPython.display import Audio\n","# Audio(\"/content/pokkiri-message-3920.mp3\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"id":"_8vKBR6cfxm6","outputId":"5c7a3e4d-708e-4ec9-cfaf-b39ffeb1f156"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<IPython.lib.display.Audio object>"],"text/html":["\n","                <audio  controls=\"controls\" >\n","                    <source 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\" type=\"audio/mpeg\" />\n","                    Your browser does not support the audio element.\n","                </audio>\n","              "]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["%%time\n","# load audio and pad/trim it to fit 30 seconds\n","audio = whisper.load_audio(\"/content/drive/MyDrive/Whisper/audio/206Elissa.mp3\")\n","audio = whisper.pad_or_trim(audio)\n","\n","# make log-Mel spectrogram and move to the same device as the model\n","mel = whisper.log_mel_spectrogram(audio).to(model.device)\n","\n","# detect the spoken language\n","_, probs = model.detect_language(mel)\n","print(f\"Detected language: {max(probs, key=probs.get)}\")\n","\n","# decode the audio\n","options = whisper.DecodingOptions()\n","result = whisper.decode(model, mel, options)\n","\n","# print the recognized text\n","print(result.text)"],"metadata":{"id":"bDXgLIprIsAj","outputId":"31fa19e1-e3a8-4d84-9259-afd07fe15cb6","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1683459684243,"user_tz":-120,"elapsed":24033,"user":{"displayName":"Lisa Ming","userId":"02034015347897270913"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Detected language: en\n","Welcome to the Huberman Lab podcast where we discuss science and science-based tools for everyday life. I'm Andrew Huberman and I'm a professor of neurobiology and Ophthalmology at Stanford School of Medicine. Today my guest is Dr. Alyssa Eppel. Dr. Eppel is a professor of psychiatry and behavioral sciences at the University of California, San Francisco. She is also the director of the Center on Aging, Metabolism and Emotions.\n","CPU times: user 4.73 s, sys: 1.88 s, total: 6.61 s\n","Wall time: 23.6 s\n"]}]},{"cell_type":"code","source":["audio.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0kEOUYtbA1om","executionInfo":{"status":"ok","timestamp":1683459690431,"user_tz":-120,"elapsed":387,"user":{"displayName":"Lisa Ming","userId":"02034015347897270913"}},"outputId":"719cff56-1360-4ea4-9a0a-3d3d0672b9a5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(480000,)"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["# \n","def transcribe(audio):\n","    \n","    #time.sleep(3)\n","    # load audio and pad/trim it to fit 30 seconds\n","    audio = whisper.load_audio(audio)\n","    audio = whisper.pad_or_trim(audio)\n","\n","    # make log-Mel spectrogram and move to the same device as the model\n","    mel = whisper.log_mel_spectrogram(audio).to(model.device)\n","\n","    # detect the spoken language\n","    _, probs = model.detect_language(mel)\n","    print(f\"Detected language: {max(probs, key=probs.get)}\")\n","\n","    # decode the audio\n","    options = whisper.DecodingOptions()\n","    result = whisper.decode(model, mel, options)\n","\n","    # # Copy the log.csv file to the output folder\n","\n","    # shutil.copy(\"/content/flagged/log.csv\", \"/content/drive/MyDrive/Whisper/output/log.csv\")\n","    return result.text\n"],"metadata":{"id":"ILFOYNnTcYe8"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Batch mp3 microphone, lonely mp3"],"metadata":{"id":"rwB8iOxLRLr5"}},{"cell_type":"code","source":["# Looks ok getting audio files from filewraper and delivering them one by one to transcribe\n","def my_function(files):\n","\n","  temp=''\n","  filenames = [file.name for file in files]\n","  for audio_file in filenames:\n","    print(audio_file)\n","    result=transcribe(audio_file)\n","    temp=temp+result\n","  return temp"],"metadata":{"id":"ZWiSeZewnUn4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["with gr.Blocks() as demo:\n","    gr.Markdown(\"Transcribe with Whisper\")\n","    with gr.Tab(\"transcribe mp3\"):\n","        mp3_input = gr.inputs.Audio(label=\"Upload Audio File (.mp3)\", type=\"filepath\")\n","        # mp3_input = gr.inputs.File(file_count=\"multiple\",label=\"Input Files\")\n","        \n","        text_output1 = gr.Textbox()\n","        text_button = gr.Button(\"Transcribe\")\n","    with gr.Tab(\"transcribe in Batch  mp3 files \"):\n","        # mp3_input = gr.inputs.Audio(label=\"Upload Audio File (.mp3)\", type=\"filepath\")\n","        mp3_batch = gr.inputs.File(file_count=\"multiple\",label=\"Input Files\")\n","        \n","        text_output3 = gr.Textbox()\n","        text_button3 = gr.Button(\"Transcribe\")\n","\n","    with gr.Tab(\"transcribe mike\"):\n","        with gr.Row():\n","            mike_input = gr.inputs.Audio(source=\"microphone\", type=\"filepath\")\n","            text_output2 = gr.Textbox()\n","        image_button = gr.Button(\"Transcribe\")\n","\n","    # with gr.Accordion(\"Open for More!\"):\n","    #     gr.Markdown(\"Look at me...\")\n","\n","    text_button.click(transcribe, inputs=mp3_input, outputs=text_output1)\n","    image_button.click(transcribe, inputs=mike_input, outputs=text_output2)\n","    text_button3.click(my_function, inputs=mp3_batch, outputs=text_output3)\n","\n","demo.launch(share=True, debug=True)"],"metadata":{"id":"kKrfMI44XIlf","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1683487834492,"user_tz":-120,"elapsed":539334,"user":{"displayName":"Lisa Ming","userId":"02034015347897270913"}},"outputId":"5515381d-7ec1-4c39-aa5b-c4abd79e84ee"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/gradio/inputs.py:321: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n","  warnings.warn(value)\n","/usr/local/lib/python3.10/dist-packages/gradio/inputs.py:349: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/gradio/deprecation.py:40: UserWarning: `keep_filename` parameter is deprecated, and it has no effect\n","  warnings.warn(value)\n"]},{"output_type":"stream","name":"stdout","text":["Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n","Running on public URL: https://a87b5781597778fdbd.gradio.live\n","\n","This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<div><iframe src=\"https://a87b5781597778fdbd.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["/tmp/gradio/161d3c43e7bc0fd80c90b71895a8cbe4b61d6981/ryszard.mp3\n","Detected language: pl\n","/tmp/gradio/198fc7818d4fa6830a45704384f1cad49ed182d0/ryszard_001.mp3\n","Detected language: pl\n","/tmp/gradio/55b9ce492d1a86b6610ec1238ae45e6c9a8ef967/ryszard_002.mp3\n","Detected language: en\n","/tmp/gradio/5c5800bd47288bf45f6959fe29a6f7074b18d922/huberman_002.mp3\n","Detected language: en\n","/tmp/gradio/c03249a9e5aff171c5ad8a9772d8997a1a0b07fd/huberman_006.mp3\n","Detected language: en\n","/tmp/gradio/bce2fe47e2f3280efe7c38e7f395597de10f0013/huberman_008.mp3\n","Detected language: en\n","/tmp/gradio/578e6ad4ea9fdd64ecc656d9463ca360a3451aa0/huberman_000.mp3\n","Detected language: en\n","/tmp/gradio/36c27d11207dea9536a9df47e552c9938b5cf6a0/huberman_001.mp3\n","Detected language: en\n","/tmp/gradio/5c5800bd47288bf45f6959fe29a6f7074b18d922/huberman_002.mp3\n","Detected language: en\n","/tmp/gradio/b3dd7239d815de08b6c4e09686f14c31f4390ec7/huberman_003.mp3\n","Detected language: en\n","/tmp/gradio/611483d6a3465de2c2feceb835373eb9fd8b99d0/huberman_004.mp3\n","Detected language: en\n","/tmp/gradio/1261d928da0586b12e68fa287f4864809c81971d/huberman_005.mp3\n","Detected language: en\n","/tmp/gradio/c03249a9e5aff171c5ad8a9772d8997a1a0b07fd/huberman_006.mp3\n","Detected language: en\n","/tmp/gradio/2530a3ff53da6ac1f7e6dfef77a6f4b6f43cf8bc/huberman_007.mp3\n","Detected language: en\n","/tmp/gradio/bce2fe47e2f3280efe7c38e7f395597de10f0013/huberman_008.mp3\n","Detected language: en\n","Detected language: pl\n","Keyboard interruption in main thread... closing server.\n","Killing tunnel 127.0.0.1:7863 <> https://a87b5781597778fdbd.gradio.live\n"]},{"output_type":"execute_result","data":{"text/plain":[]},"metadata":{},"execution_count":38}]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"1-7xrWTNpGzUQKQe05cICfcdqOvfSSrSj","timestamp":1683480416186},{"file_id":"1biAAqWe4LVZfm5R_lQ3T-Q0CkoNQ6PiA","timestamp":1683464334125},{"file_id":"1mPt7vKWvqMtBrVAmRNiXUQi1nzdVXjWi","timestamp":1683398042293},{"file_id":"https://github.com/amrrs/openai-whisper-webapp/blob/main/OpenAI_Whisper_ASR_Demo.ipynb","timestamp":1683106705515}],"collapsed_sections":["QK7x97iZXiab","pdZ6dXZdV81_","hrYKvxsYW-es","lQISoSaSW4M4","JO3eHM4hcTt0"]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.9"}},"nbformat":4,"nbformat_minor":0}