Add code generation for gpt-3 ft

gpt3_ft.py

@@ -0,0 +1,61 @@
+import youtube_dl
+import openai
+import os
+
+# Set up OpenAI credentials
+openai.api_key = "YOUR_API_KEY"
+
+# Define function to transcribe audio using OpenAI
+def transcribe_audio(audio_file):
+ response = openai.Completion.create(
+ engine="davinci",
+ prompt="Transcribe the following audio:\n" + audio_file,
+ max_tokens=2048,
+ n=1,
+ stop=None,
+ temperature=0.5,
+ )
+ return response.choices[0].text.strip()
+
+# Download YouTube video and extract audio using youtube_dl
+url = "https://www.youtube.com/watch?v=YOUR_VIDEO_ID"
+ydl_opts = {
+ 'format': 'bestaudio/best',
+ 'outtmpl': '%(id)s.%(ext)s',
+ 'postprocessors': [{
+ 'key': 'FFmpegExtractAudio',
+ 'preferredcodec': 'wav',
+ 'preferredquality': '192',
+ }],
+}
+with youtube_dl.YoutubeDL(ydl_opts) as ydl:
+ ydl.download([url])
+
+# Transcribe audio and chop up text into prompts and completions
+audio_file = "YOUR_VIDEO_ID.wav"
+transcribed_text = transcribe_audio(audio_file)
+prompt_length = 50
+completion_length = 20
+prompts = [transcribed_text[i:i+prompt_length] for i in range(0, len(transcribed_text), prompt_length)]
+completions = [transcribed_text[i:i+completion_length] for i in range(prompt_length, len(transcribed_text), completion_length)]
+
+# Fine-tune GPT-3 using prompts and completions
+model_engine = "davinci"
+model_name = "YOUR_MODEL_NAME"
+model_prompt = "\n".join(prompts)
+model_completion = "\n".join(completions)
+fine_tuned_model = openai.FineTune.create(
+ model=model_name,
+ prompt=model_prompt,
+ examples=[{"text": model_completion}],
+ temperature=0.7,
+ max_tokens=1024,
+ n_epochs=5,
+ batch_size=4,
+ learning_rate=1e-5,
+ labels=["transcription"],
+ create= True
+)
+
+# Print the fine-tuned model's ID
+print(f"Fine-tuned model ID: {fine_tuned_model.id}")