Generate_Transcripts.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":44885,"status":"ok","timestamp":1627488927888,"user":{"displayName":"Jun Xian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggd4HfOyaAKmmdA4X1zIXUeGL5_hJr44Dw5W97aWQ=s64","userId":"17208504523579366933"},"user_tz":-480},"id":"GDM6TyEA5bwi","outputId":"d68c053a-534f-4a8e-d2b3-5ec30d9102af"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n"]}],"source":["# Mount google drive\n","\n","from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"markdown","metadata":{"id":"OzsOs3qGYTEO"},"source":["### Installing dependencies"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2021-04-15T15:28:52.608071Z","iopub.status.busy":"2021-04-15T15:28:52.607460Z","iopub.status.idle":"2021-04-15T15:29:06.470713Z","shell.execute_reply":"2021-04-15T15:29:06.469773Z"},"executionInfo":{"elapsed":5226,"status":"ok","timestamp":1627488947781,"user":{"displayName":"Jun Xian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggd4HfOyaAKmmdA4X1zIXUeGL5_hJr44Dw5W97aWQ=s64","userId":"17208504523579366933"},"user_tz":-480},"id":"lzDJE67q3_8A","outputId":"5669056f-843b-45e7-fad7-ba67b8294f62","papermill":{"duration":13.881037,"end_time":"2021-04-15T15:29:06.470921","exception":false,"start_time":"2021-04-15T15:28:52.589884","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.9.1)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.6.1)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (21.0)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n","Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.3)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.41.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (5.4.1)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n","Requirement already satisfied: huggingface-hub==0.0.12 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.12)\n","Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.45)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub==0.0.12->transformers) (3.7.4.3)\n","Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.5.0)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.5.30)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n","Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n","Requirement already satisfied: pydub in /usr/local/lib/python3.7/dist-packages (0.25.1)\n"]}],"source":["!pip install --upgrade transformers\n","!pip install pydub"]},{"cell_type":"markdown","metadata":{"id":"XLjv3yIylpcg"},"source":["### Import Libraries"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2021-04-15T15:29:06.587855Z","iopub.status.busy":"2021-04-15T15:29:06.586958Z","iopub.status.idle":"2021-04-15T15:29:10.774870Z","shell.execute_reply":"2021-04-15T15:29:10.773652Z"},"id":"Uf0ex-DO3_8C","papermill":{"duration":4.223959,"end_time":"2021-04-15T15:29:10.775029","exception":false,"start_time":"2021-04-15T15:29:06.551070","status":"completed"},"tags":[]},"outputs":[],"source":["import os\n","import os.path\n","from os import path\n","\n","import seaborn as sns\n","\n","import soundfile as sf\n","import librosa\n","from pydub import AudioSegment as am\n","\n","import pandas as pd\n","import numpy as np\n","\n","import torch\n","\n","import transformers\n","\n","from transformers import (\n","    Wav2Vec2ForCTC,\n","    Wav2Vec2Processor,\n","    AutoTokenizer, \n","    AutoModelWithLMHead \n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1627488957478,"user":{"displayName":"Jun Xian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggd4HfOyaAKmmdA4X1zIXUeGL5_hJr44Dw5W97aWQ=s64","userId":"17208504523579366933"},"user_tz":-480},"id":"mJG3WcJe3_8D","outputId":"cb91ea1a-a6fa-4375-c5d1-c2111d79956e"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'4.9.1'"]},"execution_count":6,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["transformers.__version__"]},{"cell_type":"markdown","metadata":{"id":"HEtZ5MJ-nhB4"},"source":["### Load HuBERT model"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","referenced_widgets":["17b6a9dffdff49eab8f263f147f1a935","4b6e07779bef4202a35a3c0ec387ddcd","325f970968d74798b8384b800df2da6a","81f7f7997bd14b98bda24c28536e2fb0","0023cb7f4cf445b1b14bffa70d6c7dd4","2be8e9bb1400456e824617695c1c720b","f953d4c838ad41beb7ab510381882036","8d0bc0b1c11b40edb25d9d431ccdea26","a999f9b7b7ea46c0a7db67d951c93588","ba86546fd1b9404eb306d59b42db4f6a","71e7c918583443a7b05e984905743cac","9fbafc1cc2474b8c8c70e477621fc6d1","20819353cc8a41fc9570ca5230bdc5fd","e753d451b6664963ba4b672a3c2498b8","0327fd1db6694b2baa2e2a66fabdb361","8659b727c58c4a6b9de7f46fea929062","d806c292e0fd4f399e7bd67d1283be82","3476002bdb804f4395a3a14188193d75","ec816d67810e4e3ba96262bd82b4fee1","0d9649dc72fe414c97640743ca90e0b2","3d968465703647cfa75b92a501e6925a","a34b91b6c1c84b53822607fcd88b09e5","9aed44dc083045cb8edd56c21c5c3074","5530f9cb396341e383674046a89b3740","4b0d3594978a44c2864ff098c9e61afc","16584053dd2e4437b99b794773b0f0eb","9ec4bad9cc954b49ac09fb4b17736646","5e7c26efb48b452cbe4626463a8aa106","a3b0ca66ff234f95b89d6331da6c9b1f","19984abe459240eb8514bc1a3abd612c","4236bcf51a9347f28a81e6c160818640","48943cab3a404286b960e2a10143b74a","c2790b83da8e451a8f3e3a015315887c","44302f478855409db75e08472a974c08","f58c427a19ba4fc09d6e60c97859ddb4","e4b191f1273c42479c1ac51891447b7a","4eaabd5a588e49ab98ee14d5d8343bc1","55bba5a57b664e02abf75beb2be9cea8","0ef61f934eed411db472f542e3fcbb75","9b44ac521d214626bddabbfe629b7803","c30e7181190d4316bc7a1ed662e6a3e5","feda5ce530c04af4add5e04cb1461a85","b7fd03d3cacd4c0884523fd9cb9f913e","d891feed547f422b9ca7c19ece49b03f","49e62da69b4b4e3e9fc50cd444f129ef","373af164ce644035bb13f81c705107ae","48905cdec8ce43bd80972b3781184ffd","acf97f6dd3be4867aecfe967a720cf4c"]},"executionInfo":{"elapsed":80999,"status":"ok","timestamp":1627489039284,"user":{"displayName":"Jun Xian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggd4HfOyaAKmmdA4X1zIXUeGL5_hJr44Dw5W97aWQ=s64","userId":"17208504523579366933"},"user_tz":-480},"id":"bfFOygsAnkWg","outputId":"56e4c1a0-594c-440b-833c-f32eb1cfdcd9"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"17b6a9dffdff49eab8f263f147f1a935","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=212.0, style=ProgressStyle(description_…"]},"metadata":{"tags":[]},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a999f9b7b7ea46c0a7db67d951c93588","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=292.0, style=ProgressStyle(description_…"]},"metadata":{"tags":[]},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d806c292e0fd4f399e7bd67d1283be82","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=138.0, style=ProgressStyle(description_…"]},"metadata":{"tags":[]},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4b0d3594978a44c2864ff098c9e61afc","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=85.0, style=ProgressStyle(description_w…"]},"metadata":{"tags":[]},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c2790b83da8e451a8f3e3a015315887c","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1404.0, style=ProgressStyle(description…"]},"metadata":{"tags":[]},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c30e7181190d4316bc7a1ed662e6a3e5","version_major":2,"version_minor":0},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=3850480983.0, style=ProgressStyle(descr…"]},"metadata":{"tags":[]},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n"]}],"source":["from transformers import HubertForCTC\n","\n","processor = Wav2Vec2Processor.from_pretrained(\"facebook/hubert-xlarge-ls960-ft\")\n","model = HubertForCTC.from_pretrained(\"facebook/hubert-xlarge-ls960-ft\")"]},{"cell_type":"markdown","metadata":{"id":"0IomkK9-6aUS"},"source":["### Resampling to 16kHz"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0IcMsm_5GIyG"},"outputs":[],"source":["def pre_proc(input_file):\n","  sr = 16000\n","\n","  # If current file is not 16kHz, resample \n","  if librosa.get_samplerate(input_file) != sr:\n","    sound = am.from_file(input_file, format='wav', frame_rate=44100)\n","    sound = sound.set_frame_rate(16000)\n","    sound.export(input_file, format='wav')\n","    print(f'Resampled to {sr}')\n"]},{"cell_type":"markdown","metadata":{"id":"FowGFTaKPGRK"},"source":["### Setting up CTC beam search decoding"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":90227,"status":"ok","timestamp":1627489129504,"user":{"displayName":"Jun Xian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggd4HfOyaAKmmdA4X1zIXUeGL5_hJr44Dw5W97aWQ=s64","userId":"17208504523579366933"},"user_tz":-480},"id":"G_CtBu-YPPZF","outputId":"1457e2b1-6744-47dc-b29f-f745cd13abd0"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2021-07-28 16:17:18--  https://www.openslr.org/resources/11/4-gram.arpa.gz\n","Resolving www.openslr.org (www.openslr.org)... 46.101.158.64\n","Connecting to www.openslr.org (www.openslr.org)|46.101.158.64|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1355172078 (1.3G) [application/x-gzip]\n","Saving to: ‘4-gram.arpa.gz’\n","\n","4-gram.arpa.gz      100%[===================>]   1.26G  26.4MB/s    in 50s     \n","\n","2021-07-28 16:18:09 (25.9 MB/s) - ‘4-gram.arpa.gz’ saved [1355172078/1355172078]\n","\n"]}],"source":["# https://github.com/flashlight/wav2letter/tree/master/recipes/sota/2019\n","\n","# Set n=4 to use a 4-gram LM to decoding \n","n=4\n","\n","# Download the 4-gram LM from openslr\n","if n == 4:\n","  if not path.exists(\"/content/4-gram.arpa.gz\"):\n","    !wget https://www.openslr.org/resources/11/4-gram.arpa.gz\n","    !gunzip '/content/4-gram.arpa.gz'\n","    mp = '/content/4-gram.arpa'\n","  else:\n","    mp = '/content/4-gram.arpa'\n","elif n == 3:\n","  if not path.exists(\"/content/3-gram.pruned.3e-7.arpa.gz\"):\n","    !wget https://www.openslr.org/resources/11/3-gram.pruned.3e-7.arpa.gz\n","    !gunzip '/content/3-gram.pruned.3e-7.arpa.gz'\n","    mp = '/content/3-gram.pruned.3e-7.arpa'\n","  else:\n","    mp = '/content/3-gram.pruned.3e-7.arpa'"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":181889,"status":"ok","timestamp":1627489311386,"user":{"displayName":"Jun Xian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggd4HfOyaAKmmdA4X1zIXUeGL5_hJr44Dw5W97aWQ=s64","userId":"17208504523579366933"},"user_tz":-480},"id":"BbjTl9cOTNE3","outputId":"d7cb00a1-debd-4c76-80ff-44be57d948c3"},"outputs":[{"name":"stdout","output_type":"stream","text":["/content\n","Cloning into 'ctcdecode'...\n","remote: Enumerating objects: 1102, done.\u001b[K\n","remote: Counting objects: 100% (39/39), done.\u001b[K\n","remote: Compressing objects: 100% (26/26), done.\u001b[K\n","remote: Total 1102 (delta 16), reused 28 (delta 13), pack-reused 1063\u001b[K\n","Receiving objects: 100% (1102/1102), 780.91 KiB | 6.45 MiB/s, done.\n","Resolving deltas: 100% (529/529), done.\n","Submodule 'third_party/ThreadPool' (https://github.com/progschj/ThreadPool.git) registered for path 'third_party/ThreadPool'\n","Submodule 'third_party/kenlm' (https://github.com/kpu/kenlm.git) registered for path 'third_party/kenlm'\n","Cloning into '/content/ctcdecode/third_party/ThreadPool'...\n","remote: Enumerating objects: 82, done.        \n","remote: Total 82 (delta 0), reused 0 (delta 0), pack-reused 82        \n","Cloning into '/content/ctcdecode/third_party/kenlm'...\n","remote: Enumerating objects: 14047, done.        \n","remote: Counting objects: 100% (360/360), done.        \n","remote: Compressing objects: 100% (292/292), done.        \n","remote: Total 14047 (delta 107), reused 121 (delta 55), pack-reused 13687        \n","Receiving objects: 100% (14047/14047), 5.76 MiB | 19.98 MiB/s, done.\n","Resolving deltas: 100% (7987/7987), done.\n","Submodule path 'third_party/ThreadPool': checked out '9a42ec1329f259a5f4881a291db1dcb8f2ad9040'\n","Submodule path 'third_party/kenlm': checked out '35835f1ac4884126458ac89f9bf6dd9ccad561e0'\n","Processing ./ctcdecode\n","\u001b[33m  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n","   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n","Building wheels for collected packages: ctcdecode\n","  Building wheel for ctcdecode (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for ctcdecode: filename=ctcdecode-1.0.3-cp37-cp37m-linux_x86_64.whl size=13247817 sha256=684dacb80d1eef1ac352fd6ff6b3f926985ffd6d4f848c540aca07c80211b65b\n","  Stored in directory: /tmp/pip-ephem-wheel-cache-0j25x8cg/wheels/da/bb/b4/233de9fd7927245208e27bcf688bf5680ae3f3874be2895eef\n","Successfully built ctcdecode\n","Installing collected packages: ctcdecode\n","Successfully installed ctcdecode-1.0.3\n","/content/ctcdecode\n","Processing /content/ctcdecode\n","\u001b[33m  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n","   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n","Building wheels for collected packages: ctcdecode\n","  Building wheel for ctcdecode (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for ctcdecode: filename=ctcdecode-1.0.3-cp37-cp37m-linux_x86_64.whl size=13247813 sha256=ead6ac933e0fd3178bb84a2013e0c9307dc753a512278ce279494f937b74eec5\n","  Stored in directory: /tmp/pip-ephem-wheel-cache-1807oh4_/wheels/da/bb/b4/233de9fd7927245208e27bcf688bf5680ae3f3874be2895eef\n","Successfully built ctcdecode\n","Installing collected packages: ctcdecode\n","  Attempting uninstall: ctcdecode\n","    Found existing installation: ctcdecode 1.0.3\n","    Uninstalling ctcdecode-1.0.3:\n","      Successfully uninstalled ctcdecode-1.0.3\n","Successfully installed ctcdecode-1.0.3\n"]}],"source":["# Install CTC beam search decoding\n","\n","if not path.exists(\"/content/ctcdecode\"):\n","  %cd /content \n","  !git clone --recursive https://github.com/parlance/ctcdecode.git  \n","  !pip install /content/ctcdecode #takes about 5 minutes on google colab\n","\n","  %cd ctcdecode\n","  !pip install ."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1627489311386,"user":{"displayName":"Jun Xian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggd4HfOyaAKmmdA4X1zIXUeGL5_hJr44Dw5W97aWQ=s64","userId":"17208504523579366933"},"user_tz":-480},"id":"vHvP7J29m3tZ","outputId":"cc0af251-021a-43f6-bb6a-4de43c4415dd"},"outputs":[{"name":"stdout","output_type":"stream","text":["/content/ctcdecode/ctcdecode\n"]}],"source":["cd ctcdecode"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qb5uFY9dPzcz"},"outputs":[],"source":["# Import CTC beam search decoder\n","\n","from ctcdecode import CTCBeamDecoder\n","\n","vocab_dict = processor.tokenizer.get_vocab()\n","sort_vocab = sorted((value, key) for (key,value) in vocab_dict.items())\n","vocab = [x[1].replace(\"|\", \" \") for x in sort_vocab]\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UJupQ1rS7TFv"},"outputs":[],"source":["# CTC settings\n","\n","alpha = 1 # LM Weight\n","beta = 2 # LM Usage Reward\n","\n","decoder = CTCBeamDecoder(\n","    vocab,\n","    model_path=mp,\n","    alpha=alpha,\n","    beta=beta,\n","    cutoff_top_n=40,\n","    cutoff_prob=1.0,\n","    beam_width=128,\n","    num_processes=4,\n","    blank_id=0,\n","    log_probs_input=True,\n",")"]},{"cell_type":"markdown","metadata":{"id":"mwHw_UxnsjcB"},"source":["### Generate transcript functions"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hxV0MnElO8CN"},"outputs":[],"source":["# Function used to generate text transcripts\n","\n","def asr_transcript_ngram(processor, model, input_file):\n","  sr = 16000\n","  transcript = \"\"\n","\n","  # Pre-processing\n","  pre_proc(input_file)\n","\n","  # Stream over _ seconds chunks rather than load the full file\n","  stream = librosa.stream(\n","      input_file,\n","      block_length=15,\n","      frame_length=16000,\n","      hop_length=16000\n","      )\n","\n","  for speech in stream:\n","    if len(speech.shape) > 1:\n","      speech = speech[:, 0] + speech[:, 1]\n","\n","    # Tokenize\n","    input_values = processor(speech, sampling_rate=sr, return_tensors=\"pt\").input_values\n","    # input_values = input_values.to('cuda')\n","\n","    # Retrieve Logits\n","    with torch.no_grad():\n","      logits = model(input_values).logits\n","\n","    # Beam Search\n","    beam_results, beam_scores, timesteps, out_lens= decoder.decode(logits)\n","\n","    beam_results = beam_results[:,0,:] # taking only top beam\n","    beam_results = beam_results.clip(0,32)\n","    beam_results[beam_results==32] = 0\n","    \n","\n","    # Decode\n","    transcription = processor.batch_decode(beam_results,   \n","                                          skip_special_tokens=True,\n","                                          clean_up_tokenization_spaces=False,\n","                                          group_tokens=False,\n","                                          )\n","    \n","    for i in range(len(transcription)):\n","      transcript += transcription[i] + '. '\n","    print(transcript)\n","\n","  return transcript"]},{"cell_type":"markdown","metadata":{"id":"Jm5vSBeEW7EM"},"source":["###### Function to drop segment rows < threshold"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5hLZ4Y8mW6Nl"},"outputs":[],"source":["def remove_seg(df):\n","  # Placeholder \n","  df.length = 1\n","  # For each row in df\n","  for i in range(df.shape[0]):\n","    # Calculate length of each segment\n","    df.loc[i, 'length'] = (df.end[i] - df.begin[i]) / 1000\n","\n","    # If segment length shorter than threshold, drop row\n","    df.drop(df.loc[df['length'] < 0.1].index, inplace=True)\n","\n","  return df"]},{"cell_type":"markdown","metadata":{"id":"a1AZjHTzPKog"},"source":["###### Function to generate text for each segment"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"73Jy07smyZBF"},"outputs":[],"source":["def segment_trans(df):\n","  # For each row in df\n","  for i in range(df.shape[0]):\n","    # Split the audio file into segments\n","    split = a[df.begin[i]:df.end[i]]\n","\n","    # Store the segmented audio file as temp wav file\n","    temp_segment = '/content/temp'\n","    split.export(temp_segment, format='wav')\n","\n","    print(\"Transcribing segment...\")\n","\n","    # Transcribing the segmented audio file\n","\n","    transcript = asr_transcript_ngram(processor, model, temp_segment)\n","\n","    # Appending the transcript based on the segmented parts\n","    df.loc[i, 'transcript'] = transcript\n","\n","  return df"]},{"cell_type":"markdown","metadata":{"id":"pltBWBt3sjcC"},"source":["### Generate Transcripts"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":515,"status":"ok","timestamp":1627302156164,"user":{"displayName":"Jun Xian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggd4HfOyaAKmmdA4X1zIXUeGL5_hJr44Dw5W97aWQ=s64","userId":"17208504523579366933"},"user_tz":-480},"id":"84VL_YUX4NE_","outputId":"ecd1c4e3-d5fe-4fff-a2ec-cc2020ab41b8"},"outputs":[{"name":"stdout","output_type":"stream","text":["Successfully created the directory\n"]}],"source":["# Create output folders for transcriptions\n","\n","try:\n","    os.mkdir('/content/drive/MyDrive/ADReSSo21/diagnosis/train/hubert/transcribed/')\n","    os.mkdir('/content/drive/MyDrive/ADReSSo21/diagnosis/train/hubert/transcribed/ad/')\n","    os.mkdir('/content/drive/MyDrive/ADReSSo21/diagnosis/train/hubert/transcribed/cn/')\n","except OSError:\n","    print (\"Creation of the directory failed\")\n","else:\n","    print (\"Successfully created the directory\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dcjc9vyn6Zok"},"outputs":[],"source":["\n","# Generate transcripts\n","audio_files = \"/content/drive/MyDrive/ADReSSo21/diagnosis/train/audio/\"\n","segment_files = \"/content/drive/MyDrive/ADReSSo21/diagnosis/train/segmentation/\"\n","\n","for folder in sorted(os.listdir(segment_files)):\n","  for file in sorted(os.listdir(segment_files+folder)):\n","\n","    # For each audio file\n","    audio = os.path.splitext(file)[0] + '.wav'\n","    print(f\"Transcribing {audio}...\")\n","\n","    df = pd.read_csv(segment_files+folder+'/'+file)\n","    a = am.from_file(audio_files+folder+'/'+audio)\n","\n","    # Remove segment rows < threshold\n","    remove_seg(df)\n","\n","    # Generate transcript for each segment\n","    segment_trans(df)\n","\n","    # Export generated text transcripts\n","    df.to_csv(f'/content/drive/MyDrive/ADReSSo21/diagnosis/train/hubert/transcribed/{folder}/{file}')\n","    print(f\"Transcription for {audio} done. File exported.\")"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["OzsOs3qGYTEO","XLjv3yIylpcg","HEtZ5MJ-nhB4","0IomkK9-6aUS","FowGFTaKPGRK","mwHw_UxnsjcB","pltBWBt3sjcC"],"machine_shape":"hm","name":"Generate_Transcripts.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 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