Spaces:
Running
Running
File size: 11,575 Bytes
d202ada |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 |
{
"name": "Lonely Stonebraker",
"description": "Design Dialogues with Langflow.",
"data": {
"nodes": [
{
"width": 384,
"height": 461,
"id": "CustomComponent-MtJjl",
"type": "genericNode",
"position": {
"x": 534.3712097224906,
"y": -135.01908566635723
},
"data": {
"type": "CustomComponent",
"node": {
"template": {
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "from langflow.custom import CustomComponent\nfrom langflow.field_typing import Data\nfrom pathlib import Path\nfrom platformdirs import user_cache_dir\nimport os\n\nclass Component(CustomComponent):\n documentation: str = \"http://docs.langflow.org/components/custom\"\n\n def build_config(self):\n return {\"text_input\":{\"display_name\":\"Text Input\", \"input_types\":[\"str\"]},\"save_path\":{\"display_name\":\"Save Path\",\n \"info\":\"Put the full path with the file name and extension\",\"value\":Path(user_cache_dir(\"langflow\"))/\"text.t1.txt\"}}\n\n def build(self, text_input:str,save_path:str) -> str:\n try:\n # Create the directory if it doesn't exist\n os.makedirs(os.path.dirname(save_path), exist_ok=True)\n\n # Open the file in write mode and save the text\n with open(save_path, 'w') as file:\n file.write(text_input)\n except Exception as e:\n raise e\n self.status = text_input\n return text_input",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": false,
"dynamic": true,
"info": ""
},
"save_path": {
"type": "str",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": false,
"value": "/home/vazz/.cache/langflow/text.t1.txt",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "save_path",
"display_name": "Save Path",
"advanced": false,
"dynamic": false,
"info": "Put the full path with the file name and extension"
},
"text_input": {
"type": "str",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": false,
"fileTypes": [],
"file_path": "",
"password": false,
"name": "text_input",
"display_name": "Text Input",
"advanced": false,
"input_types": ["str"],
"dynamic": false,
"info": "",
"value": ""
},
"_type": "CustomComponent"
},
"base_classes": ["str"],
"display_name": "text checkpoint",
"documentation": "http://docs.langflow.org/components/custom",
"custom_fields": {
"save_path": null,
"text_input": null
},
"output_types": ["str"],
"field_formatters": {},
"beta": true
},
"id": "CustomComponent-MtJjl"
},
"selected": false,
"dragging": false,
"positionAbsolute": {
"x": 534.3712097224906,
"y": -135.01908566635723
}
},
{
"width": 384,
"height": 453,
"id": "CustomComponent-7NQoq",
"type": "genericNode",
"position": {
"x": 27.487979888011637,
"y": -414.43998171034826
},
"data": {
"type": "CustomComponent",
"node": {
"template": {
"audio": {
"type": "file",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": false,
"fileTypes": [],
"file_path": "/home/vazz/.cache/langflow/1b0814b7-2964-4e09-9b4b-f7413c4fb50b/b56b043d8940daecbdec03b97ad4346488c58d7cc62016560dd333aa7a6a12ce.m4a",
"password": false,
"name": "audio",
"display_name": "audio",
"advanced": false,
"dynamic": false,
"info": "",
"value": "Audio Recording 2023-12-13 at 16.35.22.m4a"
},
"OpenAIKey": {
"type": "str",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": false,
"fileTypes": [],
"file_path": "",
"password": true,
"name": "OpenAIKey",
"display_name": "OpenAIKey",
"advanced": false,
"dynamic": false,
"info": "",
"value": ""
},
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "from langflow.custom import CustomComponent\nfrom typing import Optional, List, Dict, Union\nfrom langflow.field_typing import (\n AgentExecutor,\n BaseChatMemory,\n BaseLanguageModel,\n BaseLLM,\n BaseLoader,\n BaseMemory,\n BaseOutputParser,\n BasePromptTemplate,\n BaseRetriever,\n Callable,\n Chain,\n ChatPromptTemplate,\n Data,\n Document,\n Embeddings,\n NestedDict,\n Object,\n PromptTemplate,\n TextSplitter,\n Tool,\n VectorStore,\n)\n\nfrom openai import OpenAI\nimport os\nimport ffmpeg\n\nclass Component(CustomComponent):\n display_name: str = \"Whisper Transcriber\"\n description: str = \"Converts audio to text using OpenAI's Whisper.\"\n\n def build_config(self):\n return {\"audio\": {\"field_type\": \"file\", \"suffixes\": [\".mp3\", \".mp4\", \".m4a\"]}, \"OpenAIKey\": {\"field_type\": \"str\", \"password\": True}}\n\n def calculate_segment_duration(self, audio_path, target_chunk_size_mb=24):\n # Calculate the target chunk size in bytes\n target_chunk_size_bytes = target_chunk_size_mb * 1024 * 1024\n\n # Use ffprobe to get the audio file information\n ffprobe_output = ffmpeg.probe(audio_path)\n print(ffprobe_output)\n # Convert duration to float\n duration = float(ffprobe_output[\"format\"][\"duration\"])\n\n # Calculate the approximate bitrate\n bitrate = os.path.getsize(audio_path) / duration\n\n # Calculate the segment duration to achieve the target chunk size\n segment_duration = target_chunk_size_bytes / bitrate\n\n return segment_duration\n\n def split_audio_into_chunks(self, audio_path, target_chunk_size_mb=24):\n # Calculate the segment duration\n segment_duration = self.calculate_segment_duration(audio_path, target_chunk_size_mb)\n\n # Create a directory to store the chunks\n output_directory = f\"{os.path.splitext(audio_path)[0]}_chunks\"\n os.makedirs(output_directory, exist_ok=True)\n\n # Use ffmpeg-python to split the audio file into chunks\n (\n ffmpeg.input(audio_path)\n .output(f\"{output_directory}/%03d{os.path.splitext(audio_path)[1]}\", codec=\"copy\", f=\"segment\", segment_time=segment_duration)\n .run()\n )\n\n # Get the list of generated chunk files\n chunks = [os.path.join(output_directory, file) for file in os.listdir(output_directory)]\n\n return chunks\n\n def build(self, audio: str, OpenAIKey: str) -> str:\n # Split audio into chunks\n audio_chunks = self.split_audio_into_chunks(audio)\n\n client = OpenAI(api_key=OpenAIKey)\n transcripts = []\n\n try:\n for chunk in audio_chunks:\n with open(chunk, \"rb\") as chunk_file:\n transcript = client.audio.transcriptions.create(\n model=\"whisper-1\",\n file=chunk_file,\n response_format=\"text\"\n )\n transcripts.append(transcript)\n finally:\n # Clean up temporary chunk files\n for chunk in audio_chunks:\n os.remove(chunk)\n\n # Concatenate transcripts into the final response\n final_response = \"\\n\".join(transcripts)\n self.status = final_response\n return final_response\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": false,
"dynamic": true,
"info": ""
},
"_type": "CustomComponent"
},
"description": "Converts audio to text using OpenAI's Whisper.",
"base_classes": ["str"],
"display_name": "Whisper Transcriber",
"documentation": "",
"custom_fields": {
"OpenAIKey": null,
"audio": null
},
"output_types": ["str"],
"field_formatters": {},
"beta": true
},
"id": "CustomComponent-7NQoq"
},
"selected": true,
"positionAbsolute": {
"x": 27.487979888011637,
"y": -414.43998171034826
},
"dragging": false
}
],
"edges": [
{
"source": "CustomComponent-7NQoq",
"sourceHandle": "{œbaseClassesœ:[œstrœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-7NQoqœ}",
"target": "CustomComponent-MtJjl",
"targetHandle": "{œfieldNameœ:œtext_inputœ,œidœ:œCustomComponent-MtJjlœ,œinputTypesœ:[œstrœ],œtypeœ:œstrœ}",
"data": {
"targetHandle": {
"fieldName": "text_input",
"id": "CustomComponent-MtJjl",
"inputTypes": ["str"],
"type": "str"
},
"sourceHandle": {
"baseClasses": ["str"],
"dataType": "CustomComponent",
"id": "CustomComponent-7NQoq"
}
},
"style": {
"stroke": "#555"
},
"className": "stroke-gray-900 stroke-connection",
"animated": false,
"id": "reactflow__edge-CustomComponent-7NQoq{œbaseClassesœ:[œstrœ],œdataTypeœ:œCustomComponentœ,œidœ:œCustomComponent-7NQoqœ}-CustomComponent-MtJjl{œfieldNameœ:œtext_inputœ,œidœ:œCustomComponent-MtJjlœ,œinputTypesœ:[œstrœ],œtypeœ:œstrœ}"
}
],
"viewport": {
"x": 119.37759169012509,
"y": 351.3082742479685,
"zoom": 1
}
},
"is_component": false,
"updated_at": "2023-12-13T23:51:56.874099",
"folder": null,
"id": "1b0814b7-2964-4e09-9b4b-f7413c4fb50b",
"user_id": "8b5cf798-f1b8-4108-88fd-d7274d08d471"
}
|