chore: import upstream snapshot with attribution

examples/speech_graph/.env.example

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+# OpenAI API Configuration
+OPENAI_API_KEY=your-openai-api-key-here
+
+# Whisper API Configuration (Optional)
+WHISPER_API_KEY=your-whisper-api-key-here
+
+# Optional Configurations
+MAX_TOKENS=4000
+MODEL_NAME=gpt-4-1106-preview
+TEMPERATURE=0.7
+
+# Speech Settings
+AUDIO_FORMAT=mp3
+SAMPLE_RATE=16000

examples/speech_graph/README.md

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+# Speech Graph Example
+
+This example demonstrates how to use Scrapegraph-ai for speech processing and analysis.
+
+## Features
+
+- Speech-to-text conversion
+- Audio processing
+- Text analysis
+- Sentiment analysis
+
+## Setup
+
+1. Install required dependencies
+2. Copy `.env.example` to `.env`
+3. Configure your API keys in the `.env` file
+
+## Usage
+
+```python
+from scrapegraphai.graphs import SpeechGraph
+
+graph = SpeechGraph()
+text = graph.process("audio_file.mp3")
+```
+
+## Environment Variables
+
+Required environment variables:
+- `OPENAI_API_KEY`: Your OpenAI API key
+- `WHISPER_API_KEY`: Your Whisper API key (optional)

examples/speech_graph/speech_graph_openai.py

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+"""
+Basic example of scraping pipeline using SpeechSummaryGraph
+"""
+
+import os
+
+from dotenv import load_dotenv
+
+from scrapegraphai.graphs import SpeechGraph
+from scrapegraphai.utils import prettify_exec_info
+
+load_dotenv()
+
+# ************************************************
+# Define audio output path
+# ************************************************
+
+FILE_NAME = "website_summary.mp3"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+output_path = os.path.join(curr_dir, FILE_NAME)
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+openai_key = os.getenv("OPENAI_API_KEY")
+
+graph_config = {
+    "llm": {
+        "api_key": openai_key,
+        "model": "openai/gpt-4o",
+        "temperature": 0.7,
+    },
+    "tts_model": {"api_key": openai_key, "model": "tts-1", "voice": "alloy"},
+    "output_path": output_path,
+}
+
+# ************************************************
+# Create the SpeechGraph instance and run it
+# ************************************************
+
+speech_graph = SpeechGraph(
+    prompt="Make a detailed audio summary of the projects.",
+    source="https://perinim.github.io/projects/",
+    config=graph_config,
+)
+
+result = speech_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = speech_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))