machine-learning/nlp/speech-recognition-transformers/AutomaticSpeechRecognition_PythonCodeTutorial.ipynb

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          "text": [
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            "Installing collected packages: pydub\n",
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          ]
        }
      ],
      "source": [
        "!pip install transformers==4.11.2 datasets soundfile sentencepiece torchaudio pyaudio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "IA7sFGYoywJv"
      },
      "outputs": [],
      "source": [
        "from transformers import *\n",
        "import torch\n",
        "import soundfile as sf\n",
        "# import librosa\n",
        "import os\n",
        "import torchaudio"
      ]
    },
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          "metadata": {},
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'Wav2Vec2CTCTokenizer'. \n",
            "The class this function is called from is 'Wav2Vec2Tokenizer'.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/tokenization_wav2vec2.py:423: FutureWarning: The class `Wav2Vec2Tokenizer` is deprecated and will be removed in version 5 of Transformers. Please use `Wav2Vec2Processor` or `Wav2Vec2CTCTokenizer` instead.\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "data": {
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              "model_id": "444cd62dd6f345d9a714085ea14c1682",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "Downloading:   0%|          | 0.00/1.18G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-large-960h-lv60-self and are newly initialized: ['wav2vec2.masked_spec_embed']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "# model_name = \"facebook/wav2vec2-base-960h\" # 360MB\n",
        "model_name = \"facebook/wav2vec2-large-960h-lv60-self\" # 1.18GB\n",
        "\n",
        "processor = Wav2Vec2Processor.from_pretrained(model_name)\n",
        "model = Wav2Vec2ForCTC.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "GdEIJtkzEzSN"
      },
      "outputs": [],
      "source": [
        "# audio_url = \"http://www.fit.vutbr.cz/~motlicek/sympatex/f2bjrop1.0.wav\"\n",
        "# audio_url = \"http://www.fit.vutbr.cz/~motlicek/sympatex/f2bjrop1.1.wav\"\n",
        "# audio_url = \"http://www.fit.vutbr.cz/~motlicek/sympatex/f2btrop6.0.wav\"\n",
        "# audio_url = \"https://github.com/x4nth055/pythoncode-tutorials/raw/master/machine-learning/speech-recognition/16-122828-0002.wav\"\n",
        "audio_url = \"https://github.com/x4nth055/pythoncode-tutorials/raw/master/machine-learning/speech-recognition/30-4447-0004.wav\"\n",
        "# audio_url = \"https://github.com/x4nth055/pythoncode-tutorials/raw/master/machine-learning/speech-recognition/7601-291468-0006.wav\"\n",
        "# audio_url = \"https://file-examples-com.github.io/uploads/2017/11/file_example_WAV_1MG.wav\"\n",
        "# audio_url = \"http://www0.cs.ucl.ac.uk/teaching/GZ05/samples/lathe.wav\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pFm8rwjMt7TC",
        "outputId": "32fc4e5d-6e2a-4c51-d780-fb6fb53a0af2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(16000, torch.Size([274000]))"
            ]
          },
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# load our wav file\n",
        "speech, sr = torchaudio.load(audio_url)\n",
        "speech = speech.squeeze()\n",
        "# or using librosa\n",
        "# speech, sr = librosa.load(audio_file, sr=16000)\n",
        "sr, speech.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_cwT9GL3Zji",
        "outputId": "a159c7bb-d13b-48e9-cd51-2385998e0bdf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([274000])"
            ]
          },
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# resample from whatever the audio sampling rate to 16000\n",
        "resampler = torchaudio.transforms.Resample(sr, 16000)\n",
        "speech = resampler(speech)\n",
        "speech.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qtTD3gIyeNwK",
        "outputId": "0971ac8f-f7ae-4171-bf41-255635127a27"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([1, 274000])"
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# tokenize our wav\n",
        "input_values = processor(speech, return_tensors=\"pt\", sampling_rate=16000)[\"input_values\"]\n",
        "input_values.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_O7cCe7veTgB",
        "outputId": "9ed19a1c-ae50-4ac6-a593-db13faa65d0e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([1, 856, 32])"
            ]
          },
          "execution_count": 96,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# perform inference\n",
        "logits = model(input_values)[\"logits\"]\n",
        "logits.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gx6XWoTRejR0",
        "outputId": "098cb0d6-ea48-4b7b-ea2c-dbcabdcb1426"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([1, 856])"
            ]
          },
          "execution_count": 97,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# use argmax to get the predicted IDs\n",
        "predicted_ids = torch.argmax(logits, dim=-1)\n",
        "predicted_ids.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "tyWIw6rJeyN-",
        "outputId": "c758d70c-4967-43a2-bf7a-e1e9cb20010c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'and missus goddard three ladies almost always at the service of an invitation from hartfield and who were fetched and carried home so often that mister woodhouse thought it no hardship for either james or the horses had it taken place only once a year it would have been a grievance'"
            ]
          },
          "execution_count": 98,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# decode the IDs to text\n",
        "transcription = processor.decode(predicted_ids[0])\n",
        "transcription.lower()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "Oj3dTjqnmHmf"
      },
      "outputs": [],
      "source": [
        "def get_transcription(audio_path):\n",
        "  # load our wav file\n",
        "  speech, sr = torchaudio.load(audio_path)\n",
        "  speech = speech.squeeze()\n",
        "  # or using librosa\n",
        "  # speech, sr = librosa.load(audio_file, sr=16000)\n",
        "  # resample from whatever the audio sampling rate to 16000\n",
        "  resampler = torchaudio.transforms.Resample(sr, 16000)\n",
        "  speech = resampler(speech)\n",
        "  # tokenize our wav\n",
        "  input_values = processor(speech, return_tensors=\"pt\", sampling_rate=16000)[\"input_values\"]\n",
        "  # perform inference\n",
        "  logits = model(input_values)[\"logits\"]\n",
        "  # use argmax to get the predicted IDs\n",
        "  predicted_ids = torch.argmax(logits, dim=-1)\n",
        "  # decode the IDs to text\n",
        "  transcription = processor.decode(predicted_ids[0])\n",
        "  return transcription.lower()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
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              "'and missus goddard three ladies almost always at the service of an invitation from hartfield and who were fetched and carried home so often that mister woodhouse thought it no hardship for either james or the horses had it taken place only once a year it would have been a grievance'"
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            "Recording...\n",
            "Finished recording.\n"
          ]
        }
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        "import pyaudio\n",
        "import wave\n",
        "\n",
        "# the file name output you want to record into\n",
        "filename = \"recorded.wav\"\n",
        "# set the chunk size of 1024 samples\n",
        "chunk = 1024\n",
        "# sample format\n",
        "FORMAT = pyaudio.paInt16\n",
        "# mono, change to 2 if you want stereo\n",
        "channels = 1\n",
        "# 44100 samples per second\n",
        "sample_rate = 16000\n",
        "record_seconds = 10\n",
        "# initialize PyAudio object\n",
        "p = pyaudio.PyAudio()\n",
        "# open stream object as input & output\n",
        "stream = p.open(format=FORMAT,\n",
        "                channels=channels,\n",
        "                rate=sample_rate,\n",
        "                input=True,\n",
        "                output=True,\n",
        "                frames_per_buffer=chunk)\n",
        "frames = []\n",
        "print(\"Recording...\")\n",
        "for i in range(int(sample_rate / chunk * record_seconds)):\n",
        "    data = stream.read(chunk)\n",
        "    # if you want to hear your voice while recording\n",
        "    # stream.write(data)\n",
        "    frames.append(data)\n",
        "print(\"Finished recording.\")\n",
        "# stop and close stream\n",
        "stream.stop_stream()\n",
        "stream.close()\n",
        "# terminate pyaudio object\n",
        "p.terminate()\n",
        "# save audio file\n",
        "# open the file in 'write bytes' mode\n",
        "wf = wave.open(filename, \"wb\")\n",
        "# set the channels\n",
        "wf.setnchannels(channels)\n",
        "# set the sample format\n",
        "wf.setsampwidth(p.get_sample_size(FORMAT))\n",
        "# set the sample rate\n",
        "wf.setframerate(sample_rate)\n",
        "# write the frames as bytes\n",
        "wf.writeframes(b\"\".join(frames))\n",
        "# close the file\n",
        "wf.close()"
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              "\"albertanstein was a german born theoretical physicist widely acknowledged to be one of the greatest physicists of all time anstein is best known for developing the theory of relativity but he also made important contributions to the development of the theory of quanto mechanics relativity and quantom mechanics are together the two pillars of modern physics his mass energy equivalent formula e equals m c squared which arises from relativity theory has been dubbed the world's most famous equation his work is also known for its influence on the philosophy of science he received the one thousand nineteen twenty one noble prize in physics for his serv\""
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