basic pipeline done

.ipynb_checkpoints/Pipeline-checkpoint.ipynb

@@ -0,0 +1,85 @@
+{
+ "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",
+    "   "
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/Speech_to_text-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/Testing_Models-checkpoint.ipynb

@@ -0,0 +1,235 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac7631cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import re\n",
+    "import librosa\n",
+    "from datasets import load_dataset, load_metric\n",
+    "from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor\n",
+    "import warnings\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "LANG_ID = \"zh-CN\"\n",
+    "MODEL_ID = \"zh-CN-output-aishell\"\n",
+    "\n",
+    "test_dataset = load_dataset(\"common_voice\", LANG_ID, split=\"test\")\n",
+    "\n",
+    "wer = load_metric(\"wer\")\n",
+    "cer = load_metric(\"cer\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)\n",
+    "model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)\n",
+    "model.to(DEVICE)\n",
+    "\n",
+    "# Preprocessing the datasets.\n",
+    "# We need to read the audio files as arrays\n",
+    "def speech_file_to_array_fn(batch):\n",
+    "    with warnings.catch_warnings():\n",
+    "        warnings.simplefilter(\"ignore\")\n",
+    "        speech_array, sampling_rate = librosa.load(batch[\"path\"], sr=16_000)\n",
+    "    batch[\"speech\"] = speech_array\n",
+    "    batch[\"sentence\"] = (\n",
+    "        re.sub(\"([^\\u4e00-\\u9fa5\\u0030-\\u0039])\", \"\", batch[\"sentence\"]).lower() + \" \"\n",
+    "    )\n",
+    "    return batch\n",
+    "\n",
+    "\n",
+    "test_dataset = test_dataset.map(\n",
+    "    speech_file_to_array_fn,\n",
+    "    num_proc=15,\n",
+    "    remove_columns=['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],\n",
+    ")\n",
+    "\n",
+    "# Preprocessing the datasets.\n",
+    "# We need to read the audio files as arrays\n",
+    "def evaluate(batch):\n",
+    "    inputs = processor(\n",
+    "        batch[\"speech\"], sampling_rate=16_000, return_tensors=\"pt\", padding=True\n",
+    "    )\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        logits = model(\n",
+    "            inputs.input_values.to(DEVICE),\n",
+    "            attention_mask=inputs.attention_mask.to(DEVICE),\n",
+    "        ).logits\n",
+    "\n",
+    "    pred_ids = torch.argmax(logits, dim=-1)\n",
+    "    batch[\"pred_strings\"] = processor.batch_decode(pred_ids)\n",
+    "    return batch\n",
+    "\n",
+    "\n",
+    "result = test_dataset.map(evaluate, batched=True, batch_size=8)\n",
+    "\n",
+    "predictions = [x.lower() for x in result[\"pred_strings\"]]\n",
+    "references = [x.lower() for x in result[\"sentence\"]]\n",
+    "\n",
+    "print(\n",
+    "    f\"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}\"\n",
+    ")\n",
+    "print(f\"CER: {cer.compute(predictions=predictions, references=references) * 100}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7db04701",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11/08/2022 09:41:20 - INFO - huggingsound.speech_recognition.model - Loading model...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11/08/2022 09:41:23 - WARNING - root - bos_token <s> not in provided tokens. It will be added to the list of tokens\n",
+      "11/08/2022 09:41:23 - WARNING - root - eos_token </s> not in provided tokens. It will be added to the list of tokens\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████| 1/1 [00:00<00:00,  2.11it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from huggingsound import SpeechRecognitionModel\n",
+    "model = SpeechRecognitionModel(\"./wav2vec2-large-xlsr-chinese\")\n",
+    "audio_paths = [\"1.wav\"]\n",
+    "transcriptions = model.transcribe(audio_paths)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "23316152",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'你喜欢饭吗'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# transcriptions[0]['transcription'].replace('[PAD]','')\n",
+    "transcriptions[0]['transcription']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "730d4afa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from transformers import Wav2Vec2Processor, HubertForCTC\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "processor = Wav2Vec2Processor.from_pretrained(\"./english_fine_tune\")\n",
+    "model = HubertForCTC.from_pretrained(\"./english_fine_tune\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "f45768e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "It is strongly recommended to pass the ``sampling_rate`` argument to this function. Failing to do so can result in silent errors that might be hard to debug.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import librosa\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": 26,
+   "id": "8bd98a38",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'WITHOUT THE DATA SET THE ARTICLE IS USELESS'"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transcription"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db6a5667",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Pipeline.ipynb

@@ -0,0 +1,191 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "edc2e2ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import librosa\n",
+    "import torch\n",
+    "from transformers import Wav2Vec2Processor, HubertForCTC\n",
+    "from huggingsound import SpeechRecognitionModel\n",
+    "import torchaudio\n",
+    "from speechbrain.pretrained import EncoderClassifier\n",
+    "import time\n",
+    "from transformers import Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "76f25cc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11/08/2022 14:17:47 - INFO - huggingsound.speech_recognition.model - Loading model...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11/08/2022 14:17:49 - WARNING - root - bos_token <s> not in provided tokens. It will be added to the list of tokens\n",
+      "11/08/2022 14:17:49 - WARNING - root - eos_token </s> not in provided tokens. It will be added to the list of tokens\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_chinese = SpeechRecognitionModel(\"./wav2vec2-large-xlsr-chinese\")\n",
+    "processor = Wav2Vec2Processor.from_pretrained(\"./english_fine_tune\")\n",
+    "model = HubertForCTC.from_pretrained(\"./english_fine_tune\")\n",
+    "language_id = EncoderClassifier.from_hparams(source=\"speechbrain/lang-id-voxlingua107-ecapa\", savedir=\"tmp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "3b142546",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pipeline(path_to_audio):\n",
+    "    signal = language_id.load_audio(path_to_audio)\n",
+    "    prediction =  language_id.classify_batch(signal)\n",
+    "    prediction[3]\n",
+    "    \n",
+    "    if prediction[3][0] == 'zh: Chinese':\n",
+    "        print('Detected Language is Chinese')\n",
+    "        transcriptions = model_chinese.transcribe([path_to_audio])\n",
+    "        print(transcriptions[0]['transcription'])\n",
+    "    else:\n",
+    "        print('Detected language is English')\n",
+    "        input_audio, sr = librosa.load(path_to_audio, sr = 16000)\n",
+    "        input_values = processor(input_audio, return_tensors=\"pt\").input_values  \n",
+    "        logits = model(input_values).logits\n",
+    "        predicted_ids = torch.argmax(logits, dim=-1)\n",
+    "        transcription = processor.decode(predicted_ids[0])\n",
+    "        print(transcription)\n",
+    "   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "48bed0f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "It is strongly recommended to pass the ``sampling_rate`` argument to this function. Failing to do so can result in silent errors that might be hard to debug.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Detected language is English\n",
+      "WITHOUT THE DATA SET THE ARTICLE IS USELESS\n"
+     ]
+    }
+   ],
+   "source": [
+    "class Speech_to_Text(Pipeline):\n",
+    "    def postprocess(self,model_outputs):\n",
+    "        if prediction[3][0] == 'zh: Chinese':\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "b0fae1dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "It is strongly recommended to pass the ``sampling_rate`` argument to this function. Failing to do so can result in silent errors that might be hard to debug.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Detected language is English\n",
+      "WITHOUT THE DATA SET THE ARTICLE IS USELESS\n"
+     ]
+    }
+   ],
+   "source": [
+    "start = time.time()\n",
+    "pipeline('english.wav')\n",
+    "end = time.time()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "1e0321b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5424931049346924"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "end - start"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a069a0fd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Testing_Models.ipynb

@@ -0,0 +1,235 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac7631cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import re\n",
+    "import librosa\n",
+    "from datasets import load_dataset, load_metric\n",
+    "from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor\n",
+    "import warnings\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "LANG_ID = \"zh-CN\"\n",
+    "MODEL_ID = \"zh-CN-output-aishell\"\n",
+    "\n",
+    "test_dataset = load_dataset(\"common_voice\", LANG_ID, split=\"test\")\n",
+    "\n",
+    "wer = load_metric(\"wer\")\n",
+    "cer = load_metric(\"cer\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)\n",
+    "model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)\n",
+    "model.to(DEVICE)\n",
+    "\n",
+    "# Preprocessing the datasets.\n",
+    "# We need to read the audio files as arrays\n",
+    "def speech_file_to_array_fn(batch):\n",
+    "    with warnings.catch_warnings():\n",
+    "        warnings.simplefilter(\"ignore\")\n",
+    "        speech_array, sampling_rate = librosa.load(batch[\"path\"], sr=16_000)\n",
+    "    batch[\"speech\"] = speech_array\n",
+    "    batch[\"sentence\"] = (\n",
+    "        re.sub(\"([^\\u4e00-\\u9fa5\\u0030-\\u0039])\", \"\", batch[\"sentence\"]).lower() + \" \"\n",
+    "    )\n",
+    "    return batch\n",
+    "\n",
+    "\n",
+    "test_dataset = test_dataset.map(\n",
+    "    speech_file_to_array_fn,\n",
+    "    num_proc=15,\n",
+    "    remove_columns=['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],\n",
+    ")\n",
+    "\n",
+    "# Preprocessing the datasets.\n",
+    "# We need to read the audio files as arrays\n",
+    "def evaluate(batch):\n",
+    "    inputs = processor(\n",
+    "        batch[\"speech\"], sampling_rate=16_000, return_tensors=\"pt\", padding=True\n",
+    "    )\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        logits = model(\n",
+    "            inputs.input_values.to(DEVICE),\n",
+    "            attention_mask=inputs.attention_mask.to(DEVICE),\n",
+    "        ).logits\n",
+    "\n",
+    "    pred_ids = torch.argmax(logits, dim=-1)\n",
+    "    batch[\"pred_strings\"] = processor.batch_decode(pred_ids)\n",
+    "    return batch\n",
+    "\n",
+    "\n",
+    "result = test_dataset.map(evaluate, batched=True, batch_size=8)\n",
+    "\n",
+    "predictions = [x.lower() for x in result[\"pred_strings\"]]\n",
+    "references = [x.lower() for x in result[\"sentence\"]]\n",
+    "\n",
+    "print(\n",
+    "    f\"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}\"\n",
+    ")\n",
+    "print(f\"CER: {cer.compute(predictions=predictions, references=references) * 100}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7db04701",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11/08/2022 09:41:20 - INFO - huggingsound.speech_recognition.model - Loading model...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11/08/2022 09:41:23 - WARNING - root - bos_token <s> not in provided tokens. It will be added to the list of tokens\n",
+      "11/08/2022 09:41:23 - WARNING - root - eos_token </s> not in provided tokens. It will be added to the list of tokens\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████| 1/1 [00:00<00:00,  2.11it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from huggingsound import SpeechRecognitionModel\n",
+    "model = SpeechRecognitionModel(\"./wav2vec2-large-xlsr-chinese\")\n",
+    "audio_paths = [\"1.wav\"]\n",
+    "transcriptions = model.transcribe(audio_paths)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "23316152",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'你喜欢饭吗'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# transcriptions[0]['transcription'].replace('[PAD]','')\n",
+    "transcriptions[0]['transcription']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "730d4afa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from transformers import Wav2Vec2Processor, HubertForCTC\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "processor = Wav2Vec2Processor.from_pretrained(\"./english_fine_tune\")\n",
+    "model = HubertForCTC.from_pretrained(\"./english_fine_tune\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "f45768e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "It is strongly recommended to pass the ``sampling_rate`` argument to this function. Failing to do so can result in silent errors that might be hard to debug.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import librosa\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": 26,
+   "id": "8bd98a38",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'WITHOUT THE DATA SET THE ARTICLE IS USELESS'"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transcription"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db6a5667",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

english_fine_tune

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+Subproject commit ece5fabbf034c1073acae96d5401b25be96709d8

lang-id-voxlingua107-ecapa

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+Subproject commit d771b530cec097adc0088b4dbd173e242f895464

wav2vec2-large-xlsr-chinese

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+Subproject commit 369f73139f85a98570ff74e641dc93d421a3860e