{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6afcd792",
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "import torch\n",
    "from transformers import Wav2Vec2Processor, HubertForCTC\n",
    "from huggingsound import SpeechRecognitionModel\n",
    "model = SpeechRecognitionModel(\"./wav2vec2-large-xlsr-chinese\")\n",
    "audio_paths = [\"1.wav\"]\n",
    "transcriptions = model.transcribe(audio_paths)\n",
    "\n",
    "\n",
    "input_audio, sr = librosa.load('english.wav', sr = 16000)\n",
    "input_values = processor(input_audio, return_tensors=\"pt\").input_values  # Batch size 1\n",
    "logits = model(input_values).logits\n",
    "predicted_ids = torch.argmax(logits, dim=-1)\n",
    "transcription = processor.decode(predicted_ids[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1831eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model = SpeechRecognitionModel(\"./wav2vec2-large-xlsr-chinese\")\n",
    "\n",
    "\n",
    "processor = Wav2Vec2Processor.from_pretrained(\"./english_fine_tune\")\n",
    "model = HubertForCTC.from_pretrained(\"./english_fine_tune\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b7aee3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pipeline(path_to_audio):\n",
    "    \n",
    "    \n",
    "    if :\n",
    "        input_audio, sr = librosa.load(path_to_audio, sr = 16000)\n",
    "        input_values = processor(input_audio, return_tensors=\"pt\").input_values  # Batch size 1\n",
    "        logits = model(input_values).logits\n",
    "        predicted_ids = torch.argmax(logits, dim=-1)\n",
    "        transcription = processor.decode(predicted_ids[0])\n",
    "    \n",
    "    else:\n",
    "        transcriptions = model.transcribe([path_to_audio])\n",
    "   "
   ]
  }
 ],
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