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import embed_anything
from embed_anything import EmbedData
from tqdm.autonotebook import tqdm
from pinecone import Pinecone, ServerlessSpec
import numpy as np
import os
from pinecone import PineconeApiException
import uuid
import re
import gradio as gr
audio_files = ["samples_hp0.wav", "samples_gb0.wav"]
embeddings: list[list[EmbedData]] = []
for file in audio_files:
embedding = embed_anything.embed_file(file, "Whisper-Jina")
embeddings.append(embedding)
pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
pc.delete_index("search-in-audio")
try:
index = pc.create_index(
name="search-in-audio",
dimension=768, # Replace with your model dimensions
metric="cosine", # Replace with your model metric
spec=ServerlessSpec(cloud="aws", region="us-east-1"),
)
index = pc.Index("search-in-audio")
except PineconeApiException as e:
index = pc.Index("search-in-audio")
if e.status == 409:
print("Index already exists")
else:
print(e)
## convert embeddings which is of the form EmbedData : text, embedding, metadata to the form required by pinecone which is id, values, metadata
def convert_to_pinecone_format(embeddings: list[list[EmbedData]]):
data = []
for i, embedding in enumerate(embeddings):
for j, emb in enumerate(embedding):
data.append(
{
"id": str(uuid.uuid4()),
"values": emb.embedding,
"metadata": {
"text": emb.text,
"start": emb.metadata["start"],
"end": emb.metadata["end"],
"file": re.split(r"/|\\", emb.metadata["file_name"])[-1],
},
}
)
return data
data = convert_to_pinecone_format(embeddings)
index.upsert(data)
files = ["samples_hp0.wav", "samples_gb0.wav"]
def search(query, audio):
results = []
query = embed_anything.embed_query([query], "Jina")[0]
if re.split(r"/|\\", audio)[-1] not in files:
print(file, re.split(r"/|\\", audio)[-1])
embeddings = embed_anything.embed_file(audio, "Whisper-Jina")
embeddings = convert_to_pinecone_format([embeddings])
index.upsert(embeddings)
files.append(re.split(r"/|\\", audio)[-1])
result = index.query(
vector=query.embedding,
top_k=5,
include_metadata=True,
)
for res in result.matches:
results.append(res.metadata)
formatted_results = []
for result in results:
display_text = f"""
`File: {result['file']}`
`Start: {result['start']}`
`End: {result['end']}`
Text: {result['text']}"""
formatted_results.append(display_text)
return (
formatted_results[0],
results[0]["file"],
formatted_results[1],
results[1]["file"],
formatted_results[2],
results[2]["file"],
)
demo = gr.Interface(
title="Search 🔎 in Audio 🎙️",
description="""
# Search within audio files using text queries.
## Models used:
- **Audio Decoder**: [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en)
- **Embedding Model**: [Jina Embeddings v2 base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en)
## Vector Database used: **Pinecone**
## Powered by [EmbedAnything by Starlight](https://github.com/StarlightSearch/EmbedAnything) 🚀
""",
article = "Created by [Akshay Ballal](https://www.akshaymakes.com)",
fn=search,
inputs=["text", gr.Audio(label="Audio", type="filepath")],
outputs=[
gr.Markdown(label="Text"),
gr.Audio(label="Audio", type="filepath"),
gr.Markdown(label="Text"),
gr.Audio(label="Audio", type="filepath"),
gr.Markdown(label="Text"),
gr.Audio(label="Audio", type="filepath"),
],
examples=[
["screwdriver", "samples_hp0.wav"],
["united states", "samples_gb0.wav"],
["united states", "samples_hp0.wav"],
],
)
demo.launch()
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