codebase overhaul (#697)

3 files changed, 0 insertions, 234 deletions

diff --git a/apps/transcription/README.md b/apps/transcription/README.md
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index 07be6790..00000000
--- a/apps/transcription/README.md
+++ /dev/null
@@ -1,86 +0,0 @@
-Before you follow anything in this guide, please make sure you've followed the steps in the [README](../../README.md) (under "Optional: Auto-captions (Transcription) Setup").

-

-Open your terminal and make sure you're in the `apps/transcription` directory.

-

-1. Create virtual environment

-

-```bash

-python -m venv env

-```

-

-2. Activate it

-

-**Windows:**

-

-```bash

-env\Scripts\activate

-```

-

-**macOS/Linux:**

-

-```bash

-source env/bin/activate

-```

-

-> Note: if you're using VS Code/Cursor and you're seeing errors with the imports about the modules not being found,

-> You might have to press CTRL + Shift + P -> Python: Select Interpreter -> Enter interpreter path -> Find -> env -> scripts -> python.exe

-

-3. Install libraries/packages/whatever you wanna call them

-

-```bash

-pip install -r requirements.txt

-```

-

-4. Make sure you have a Modal account. If you don't: [create one](https://modal.com/)

-

-> If you don't know what Modal is: it allows us to process the actual audio and transcribe with Whisper by providing the infra to run Python code with a lot of RAM, generally affordable.

-

-5. Once you've got a Modal accoumt, run this:

-

-```bash

-python -m modal setup

-```

-

-It's gonna open a browser so you can authenticate.

-

-6. Test it if you want to make sure it actually works:

-

-```bash

-modal run transcription.py

-```

-

-6. Deploy the function!

-

-```bash

-modal deploy transcription.py

-```

-

-7. Set the required secrets in Modal

-

-So the script we just deployed interacts with Cloudflare to do two things:

-

-- Download the audio (so it can be transcribed with Whisper)

-- Delete the file after processing (privacy)

-

-To do those things, the script needs access to these environment variables:

-```bash

-CLOUDFLARE_ACCOUNT_ID=your-account-id

-R2_ACCESS_KEY_ID=your-access-key-id

-R2_SECRET_ACCESS_KEY=your-secret-access-key

-R2_BUCKET_NAME=opencut-transcription

-```

-

-Remember, we set these earlier in `.env.local`.

-

-So let's do it:

-

-   - Go to [Modal Secrets](https://modal.com/secrets/mazewinther/main)

-   - Click "Custom" and enter "opencut-r2-secrets" for the name.

-   - Now you can just click "Import .env" and copy/paste the 4 variables from your `.env.local` file. Copy and paste these only:

-      ```bash

-      CLOUDFLARE_ACCOUNT_ID=your-account-id

-      R2_ACCESS_KEY_ID=your-access-key-id

-      R2_SECRET_ACCESS_KEY=your-secret-access-key

-      R2_BUCKET_NAME=opencut-transcription

-      ```

-    - Click "Done" and you should see some cool particles!
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diff --git a/apps/transcription/requirements.txt b/apps/transcription/requirements.txt
deleted file mode 100644
index c002e683..00000000
--- a/apps/transcription/requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-modal

-openai-whisper

-boto3

-pydantic

-cryptography
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diff --git a/apps/transcription/transcription.py b/apps/transcription/transcription.py
deleted file mode 100644
index ad55f6d4..00000000
--- a/apps/transcription/transcription.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import modal

-from pydantic import BaseModel

-

-app = modal.App("opencut-transcription")

-

-class TranscribeRequest(BaseModel):

-    filename: str

-    language: str = "auto"

-    decryptionKey: str = None

-    iv: str = None

-

-@app.function(

-    image=modal.Image.debian_slim()

-        .apt_install(["ffmpeg"])

-        .pip_install(["openai-whisper", "boto3", "fastapi[standard]", "pydantic", "cryptography"]),

-    gpu="A10G",

-    timeout=300, # 5m

-    secrets=[modal.Secret.from_name("opencut-r2-secrets")]

-)

-@modal.fastapi_endpoint(method="POST")

-def transcribe_audio(request: TranscribeRequest):

-    import whisper

-    import boto3

-    import tempfile

-    import os

-    import json

-    

-    try:

-        filename = request.filename

-        language = request.language

-        decryption_key = request.decryptionKey

-        iv = request.iv

-        

-        if not filename:

-            return {

-                "error": "Missing filename parameter"

-            }

-        

-        # Initialize R2 client

-        s3_client = boto3.client(

-            's3',

-            endpoint_url=f'https://{os.environ["CLOUDFLARE_ACCOUNT_ID"]}.r2.cloudflarestorage.com',

-            aws_access_key_id=os.environ["R2_ACCESS_KEY_ID"],

-            aws_secret_access_key=os.environ["R2_SECRET_ACCESS_KEY"],

-            region_name='auto'

-        )

-        

-        # Create temporary file for audio

-        with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as temp_file:

-            temp_path = temp_file.name

-            

-            try:

-                # Download audio from R2

-                s3_client.download_file(

-                    os.environ["R2_BUCKET_NAME"], 

-                    filename, 

-                    temp_path

-                )

-                

-                # If decryption key provided, decrypt the file directly (zero-knowledge)

-                if decryption_key and iv:

-                    import base64

-                    from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes

-                    from cryptography.hazmat.backends import default_backend

-                    

-                    # Read the encrypted file

-                    with open(temp_path, 'rb') as f:

-                        encrypted_data = f.read()

-                    

-                    # Decode the key and IV from base64

-                    key_bytes = base64.b64decode(decryption_key)

-                    iv_bytes = base64.b64decode(iv)

-                    

-                    # Decrypt the data using AES-GCM

-                    # Extract the tag (last 16 bytes) and ciphertext

-                    tag = encrypted_data[-16:]

-                    ciphertext = encrypted_data[:-16]

-                    

-                    cipher = Cipher(

-                        algorithms.AES(key_bytes),

-                        modes.GCM(iv_bytes, tag),

-                        backend=default_backend()

-                    )

-                    decryptor = cipher.decryptor()

-                    decrypted_data = decryptor.update(ciphertext) + decryptor.finalize()

-                    

-                    # Write decrypted audio back to temp file

-                    with open(temp_path, 'wb') as f:

-                        f.write(decrypted_data)

-                

-                # Load Whisper model

-                model = whisper.load_model("base")

-                

-                # Transcribe audio

-                if language == "auto":

-                    result = model.transcribe(temp_path)

-                else:

-                    result = model.transcribe(temp_path, language=language.lower())

-                

-                # Delete audio file from R2 (cleanup)

-                s3_client.delete_object(

-                    Bucket=os.environ["R2_BUCKET_NAME"],

-                    Key=filename

-                )

-                

-                # Adjust segment timing - Whisper is consistently late by ~500ms

-                adjusted_segments = []

-                for segment in result["segments"]:

-                    adjusted_segment = segment.copy()

-                    # Shift start/end times earlier by 500ms, don't go below 0

-                    adjusted_segment["start"] = max(0, segment["start"] - 0.5)

-                    adjusted_segment["end"] = max(0.5, segment["end"] - 0.5)  # Ensure duration is at least 0.5s

-                    adjusted_segments.append(adjusted_segment)

-                

-                return {

-                    "text": result["text"],

-                    "segments": adjusted_segments,

-                    "language": result["language"]

-                }

-                

-            finally:

-                # Clean up temporary file

-                if os.path.exists(temp_path):

-                    os.unlink(temp_path)

-                    

-    except Exception as e:

-        import traceback

-        print(f"Transcription error: {str(e)}")

-        print(f"Traceback: {traceback.format_exc()}")

-        

-        # Return error response that matches expected format

-        return {

-            "error": str(e),

-            "text": "",

-            "segments": [],

-            "language": "unknown"

-        }

-

-@app.local_entrypoint()

-def main():

-    # Test function - you can call this with modal run transcription.py

-    print("Transcription service is ready to deploy!")

-    print("Deploy with: modal deploy transcription.py")
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