How to Process Audio Transcripts for Insights in Conversational AI

Processing audio transcripts at scale requires robust data infrastructure that handles both structured operations and semantic understanding. Conversational AI systems demand pipelines that parse multiple transcript formats, extract structured information, and generate insights without brittle glue code or manual prompt engineering.

This guide demonstrates how to build production-ready transcript processing pipelines using Fenic, the open-source DataFrame framework from Typedef. The framework treats LLM inference as a first-class operation, enabling deterministic workflows on non-deterministic models.

Understanding Transcript Processing Requirements

Conversational AI applications require specialized capabilities beyond traditional text processing:

Format Handling

Parse SRT (SubRip Subtitle), WebVTT (Web Video Text Tracks), and generic conversation formats
Maintain temporal relationships between conversation segments
Preserve speaker identity through transformations
Handle timestamp synchronization across different formats

Semantic Analysis

Extract structured entities and relationships from unstructured dialogue
Classify conversation segments by topic, intent, or sentiment
Aggregate insights across multiple speakers and time windows
Generate summaries that preserve conversation context

Production Reliability

Batch process hundreds or thousands of transcripts efficiently
Manage rate limits across multiple model providers
Implement automatic retry logic for transient failures
Track lineage for audit trails and debugging

Fenic addresses these requirements through a DataFrame API where inference operations receive the same optimization treatment as traditional data operations.

Setting Up the Processing Environment

Install Fenic with Python 3.10, 3.11, or 3.12:

bash
pip install fenic

Configure model providers through environment variables:

bash
export OPENAI_API_KEY="your-openai-api-key"
export ANTHROPIC_API_KEY="your-anthropic-api-key"
export GOOGLE_API_KEY="your-google-api-key"

Initialize a session with semantic configuration:

python
import fenic as fc
from fenic.api.session import Session
from fenic.api.session.config import SessionConfig, SemanticConfig
from fenic.api.session.config import OpenAILanguageModel, AnthropicLanguageModel
from fenic.core.types.inference.anthropic import AnthropicLanguageModel

config = SessionConfig(
    app_name="transcript_processor",
    semantic=SemanticConfig(
        language_models={
            "mini": OpenAILanguageModel(
                model_name="gpt-4o-mini",
                rpm=300,
                tpm=150_000
            ),
            "claude": AnthropicLanguageModel(
                model_name="claude-3-5-haiku-latest",
                rpm=100,
                input_tpm=100_000,
                output_tpm=100_000
            )
        },
        default_language_model="mini"
    )
)

session = Session.get_or_create(config)

This configuration establishes model aliases that abstract provider-specific details. Rate limiting parameters (rpm for requests per minute, tpm for tokens per minute) prevent throttling while Fenic automatically batches requests and implements self-throttling to maximize throughput.

Parsing Transcript Formats

Fenic provides native support for three transcript formats through the parse_transcript function.

Supported Format Specifications

SRT Format (SubRip Subtitle)

1
00:00:01,000 --> 00:00:05,000
First line of dialogue

2
00:00:05,500 --> 00:00:10,000
Second line of dialogue

WebVTT Format (Web Video Text Tracks)

WEBVTT

Speaker 1
00:00:01.000 --> 00:00:05.000
First line of dialogue

Speaker 2
00:00:05.500 --> 00:00:10.000
Second line of dialogue

Generic Conversation Format

[00:00:01] Speaker 1: First line of dialogue
[00:00:05] Speaker 2: Second line of dialogue

Unified Parsing Implementation

python
import fenic.api.functions as fc
from fenic.api.functions import text

# Load transcript files
df = session.read.docs(
    "transcripts/**/*.txt",
    content_type="markdown",
    recursive=True
)

# Parse SRT format
parsed_df = df.select(
    fc.col("file_path"),
    text.parse_transcript(fc.col("content"), "srt").alias("segments")
)

# Explode segments into rows
segments_df = parsed_df.select(
    fc.col("file_path"),
    fc.explode(fc.col("segments")).alias("segment")
).select(
    fc.col("file_path"),
    fc.col("segment.index").alias("index"),
    fc.col("segment.speaker").alias("speaker"),
    fc.col("segment.start_time").alias("start_time"),
    fc.col("segment.end_time").alias("end_time"),
    fc.col("segment.content").alias("text")
)

The unified schema provides consistent access to:

index: Entry number (1-based indexing)
speaker: Speaker identifier when available
start_time: Timestamp in floating-point seconds
end_time: End timestamp in seconds
duration: Calculated segment duration
content: Transcript text content
format: Original format type for lineage tracking

Applying Semantic Operations to Transcripts

Fenic's semantic operators enable content understanding without custom inference code.

Classification of Conversation Segments

Categorize transcript segments by topic or intent using semantic.classify:

python
from fenic.api.functions import semantic

# Classify support call segments
classified_df = segments_df.with_column(
    "category",
    semantic.classify(
        fc.col("text"),
        ["Technical Issue", "Billing Question", "Feature Request", "General Inquiry"]
    )
)

For more precise categorization, use ClassDefinition objects with descriptions:

python
from fenic.core.types.classify import ClassDefinition

categories = [
    ClassDefinition(
        label="Technical Issue",
        description="Problems with product functionality, bugs, or technical difficulties"
    ),
    ClassDefinition(
        label="Billing Question",
        description="Questions about charges, payments, or subscriptions"
    ),
    ClassDefinition(
        label="Feature Request",
        description="Requests for new capabilities or improvements"
    ),
    ClassDefinition(
        label="General Inquiry",
        description="General questions or non-technical assistance"
    )
]

classified_df = segments_df.with_column(
    "category",
    semantic.classify(fc.col("text"), categories)
)

Sentiment Analysis Across Conversations

Analyze emotional tone with semantic.analyze_sentiment:

python
# Add sentiment scores
sentiment_df = segments_df.with_column(
    "sentiment",
    semantic.analyze_sentiment(fc.col("text"))
)

# Aggregate by speaker
speaker_sentiment = sentiment_df.group_by("speaker").agg(
    fc.count("*").alias("segment_count"),
    fc.sum(
        fc.when(fc.col("sentiment") == "positive", 1).otherwise(0)
    ).alias("positive_count"),
    fc.sum(
        fc.when(fc.col("sentiment") == "negative", 1).otherwise(0)
    ).alias("negative_count")
)

The function returns one of three values: 'positive', 'negative', or 'neutral', enabling straightforward aggregation and filtering.

Structured Information Extraction

Extract structured entities using semantic.extract with Pydantic schemas:

python
from pydantic import BaseModel, Field
from typing import List

class ActionItem(BaseModel):
    task: str = Field(description="The action to be taken")
    assignee: str = Field(description="Person responsible")
    deadline: str = Field(description="Due date if mentioned")

# Extract action items from meeting segments
actions_df = segments_df.select(
    fc.col("file_path"),
    fc.col("text"),
    semantic.extract(
        fc.col("text"),
        ActionItem
    ).alias("action_item")
).filter(
    fc.col("action_item").is_not_null()
)

The schema provides type-safe extraction where field descriptions guide the model. This approach eliminates manual JSON parsing and validation, handling schema validation, error handling, and retries automatically.

Conversation Summarization

Generate summaries using semantic.reduce for aggregation:

python
# Summarize each file's transcript
summary_df = segments_df.group_by("file_path").agg(
    semantic.reduce(
        "Summarize this conversation highlighting key decisions and outcomes",
        fc.col("text"),
        order_by=fc.col("start_time")
    ).alias("summary")
)

The order_by parameter preserves temporal sequence, ensuring summaries respect conversation flow rather than processing segments in arbitrary order.

Semantic Filtering with Natural Language

Filter transcripts using natural language predicates:

python
# Find segments discussing specific topics
relevant_df = segments_df.filter(
    semantic.predicate(
        "This text discusses product features or roadmap planning: {{ text }}",
        text=fc.col("text")
    )
)

The predicate evaluates per row, allowing complex content-based filtering without regex patterns or keyword matching.

Building Production Transcript Analysis Pipelines

Multi-File Processing Pattern

Process directories of transcripts with format detection:

python
# Load all transcripts
all_transcripts = session.read.docs(
    ["meetings/**/*.srt", "calls/**/*.vtt"],
    content_type="markdown",
    recursive=True
)

# Parse based on file extension
parsed = all_transcripts.with_column(
    "format",
    fc.when(fc.col("file_path").like("%.srt"), fc.lit("srt"))
    .when(fc.col("file_path").like("%.vtt"), fc.lit("webvtt"))
    .otherwise(fc.lit("generic"))
).with_column(
    "segments",
    text.parse_transcript(fc.col("content"), fc.col("format"))
)

# Enrich with metadata
enriched = parsed.select(
    fc.col("file_path"),
    fc.explode(fc.col("segments")).alias("seg")
).select(
    fc.regexp_extract(
        fc.col("file_path"),
        r"(\d{4}-\d{2}-\d{2})",
        1
    ).alias("date"),
    fc.col("seg.speaker").alias("speaker"),
    fc.col("seg.content").alias("text"),
    semantic.classify(
        fc.col("seg.content"),
        ["Question", "Answer", "Statement", "Action Item"]
    ).alias("utterance_type")
)

Temporal Analysis Implementation

Track sentiment changes over time with windowed aggregation:

python
# Add time windows (5-minute buckets)
windowed_df = segments_df.with_column(
    "time_bucket",
    (fc.col("start_time") / 300).cast("integer") * 300
).with_column(
    "sentiment",
    semantic.analyze_sentiment(fc.col("text"))
)

# Aggregate by time window
temporal_sentiment = windowed_df.group_by("file_path", "time_bucket").agg(
    fc.count("*").alias("segment_count"),
    fc.sum(
        fc.when(fc.col("sentiment") == "positive", 1).otherwise(0)
    ).alias("positive"),
    fc.sum(
        fc.when(fc.col("sentiment") == "negative", 1).otherwise(0)
    ).alias("negative")
)

This pattern enables identification of conversation tone shifts, useful for quality monitoring in customer support or sales calls.

Speaker Diarization Enhancement

Enrich transcripts with speaker metadata through fuzzy joins:

python
from fenic.api.functions import text as text_fn

# Fuzzy join on speaker names
joined_df = segments_df.join(
    speakers_df,
    text_fn.compute_fuzzy_ratio(
        fc.col("speaker"),
        speakers_df["speaker_name"],
        method="jaro_winkler"
    ) > 85,
    "left"
).select(
    fc.col("file_path"),
    fc.coalesce(speakers_df["full_name"], fc.col("speaker")).alias("speaker"),
    speakers_df["department"].alias("department"),
    fc.col("text"),
    fc.col("start_time")
)

Fuzzy string matching with six algorithms (Levenshtein, Jaro-Winkler, and others) handles variations in speaker name formatting without manual mapping.

Cross-Transcript Semantic Relationships

Link related discussion points across different transcripts:

python
# Extract key topics from each transcript
topics_df1 = segments_df.filter(
    fc.col("file_path").like("%meeting_1%")
).select(
    fc.col("text").alias("meeting_1_text")
)

topics_df2 = segments_df.filter(
    fc.col("file_path").like("%meeting_2%")
).select(
    fc.col("text").alias("meeting_2_text")
)

# Semantic join to find related discussions
related = topics_df1.semantic.join(
    other=topics_df2,
    predicate="""
    Meeting 1: {{ left_on }}
    Meeting 2: {{ right_on }}

    These segments discuss the same topic or decision.
    """,
    left_on=fc.col("meeting_1_text"),
    right_on=fc.col("meeting_2_text")
)

Semantic joins determine matches based on meaning rather than exact values, ideal for tracking decisions across multiple meetings or identifying recurring themes.

Creating MCP Tools for Agent Integration

The Model Context Protocol enables AI agents to access transcript analysis without leaving their execution context. Fenic's declarative tool support transforms DataFrame queries into reusable tools.

Declarative Tool Registration

python
from fenic.core.mcp.types import ToolParam
from fenic.core.types import StringType, IntegerType

# Save processed transcripts as a table
segments_df.write.save_as_table("transcripts", mode="overwrite")

# Create a search tool
session.catalog.create_tool(
    tool_name="search_transcripts",
    tool_description="Search transcript segments by keyword or phrase",
    tool_query=session.table("transcripts").filter(
        fc.col("text").like(fc.lit("%").concat(
            fc.tool_param("search_term", StringType)
        ).concat(fc.lit("%")))
    ).limit(fc.tool_param("limit", IntegerType)),
    tool_params=[
        ToolParam(
            name="search_term",
            description="Keyword or phrase to search for",
            default_value=""
        ),
        ToolParam(
            name="limit",
            description="Maximum number of results",
            default_value=10
        )
    ],
    result_limit=50
)

# Create an analytics tool
session.catalog.create_tool(
    tool_name="analyze_speaker",
    tool_description="Get statistics and sentiment for a specific speaker",
    tool_query=session.table("transcripts").filter(
        fc.col("speaker") == fc.tool_param("speaker_name", StringType)
    ).agg(
        fc.count("*").alias("total_segments"),
        fc.avg(fc.col("end_time") - fc.col("start_time")).alias("avg_segment_duration"),
        semantic.reduce(
            "Summarize this speaker's main points and communication style",
            fc.col("text"),
            order_by=fc.col("start_time")
        ).alias("summary")
    ),
    tool_params=[
        ToolParam(
            name="speaker_name",
            description="Name or identifier of the speaker",
            default_value=""
        )
    ]
)

MCP Server Deployment

Launch the server to expose tools:

python
from fenic.api.mcp import create_mcp_server, run_mcp_server_sync

# Get all registered tools
tools = session.catalog.list_tools()

# Create server
server = create_mcp_server(
    session,
    "TranscriptServer",
    user_defined_tools=tools,
    concurrency_limit=8
)

# Run with HTTP transport
run_mcp_server_sync(
    server,
    transport="http",
    stateless_http=True,
    port=8000,
    host="127.0.0.1",
    path="/mcp"
)

Agents can now query transcript data without expensive inference in user-facing paths, providing predictable response times while leveraging Fenic's batch processing capabilities.

Optimizing Performance and Costs

Batch Size Configuration

Control concurrency through model configuration:

python
config = SessionConfig(
    app_name="transcript_processor",
    semantic=SemanticConfig(
        language_models={
            "fast_model": OpenAILanguageModel(
                model_name="gpt-4o-mini",
                rpm=500,  # Higher throughput
                tpm=200_000
            )
        }
    )
)

Fenic automatically batches inference calls, but rate limit parameters ensure operations stay within provider constraints while maximizing throughput.

Selective Processing Strategy

Filter before applying expensive operations:

python
# Only analyze segments longer than 10 words
filtered_df = segments_df.filter(
    fc.length(fc.split(fc.col("text"), " ")) > 10
).with_column(
    "summary",
    semantic.summarize(fc.col("text"))
)

The query optimizer pushes filters down to reduce data volume before inference, minimizing costs.

Caching Intermediate Results

Save enriched data to avoid recomputation:

python
# Save enriched data
enriched_df.write.save_as_table("enriched_transcripts", mode="overwrite")

# Load for subsequent processing
cached_df = session.table("enriched_transcripts")

Explicit caching eliminates redundant API calls during iterative development and testing.

Metrics Tracking and Monitoring

Access built-in metrics for optimization:

python
# Access metrics
metrics = session.table("fenic_system.query_metrics")

metrics.select(
    fc.col("model"),
    fc.col("latency_ms"),
    fc.col("cost_usd"),
    fc.col("input_tokens"),
    fc.col("output_tokens")
).order_by(fc.col("cost_usd").desc()).show()

# Aggregate by operation
metrics.group_by("operation").agg(
    fc.count("*").alias("call_count"),
    fc.sum("cost_usd").alias("total_cost"),
    fc.avg("latency_ms").alias("avg_latency")
).show()

Built-in token counting and cost tracking provide visibility into resource usage for targeted optimization.

Complete Pipeline Example

python
from fenic.api.session import Session
from fenic.api.session.config import SessionConfig, SemanticConfig
from fenic.api.session.config import OpenAILanguageModel, AnthropicLanguageModel
import fenic.api.functions as fc
from fenic.api.functions import text, semantic
from pydantic import BaseModel, Field
from typing import List

# 1. Setup
config = SessionConfig(
    app_name="transcript_analysis",
    semantic=SemanticConfig(
        language_models={
            "mini": OpenAILanguageModel(
                model_name="gpt-4o-mini",
                rpm=300,
                tpm=150_000
            )
        }
    )
)

session = Session.get_or_create(config)

# 2. Load and parse transcripts
raw_df = session.read.docs(
    "transcripts/**/*.srt",
    content_type="markdown",
    recursive=True
)

parsed_df = raw_df.select(
    fc.col("file_path"),
    text.parse_transcript(fc.col("content"), "srt").alias("segments")
)

segments_df = parsed_df.select(
    fc.col("file_path"),
    fc.explode(fc.col("segments")).alias("seg")
).select(
    fc.col("file_path"),
    fc.col("seg.speaker").alias("speaker"),
    fc.col("seg.start_time").alias("start_time"),
    fc.col("seg.content").alias("text")
)

# 3. Enrich with semantic analysis
enriched_df = segments_df.with_column(
    "sentiment",
    semantic.analyze_sentiment(fc.col("text"))
).with_column(
    "category",
    semantic.classify(
        fc.col("text"),
        ["Technical", "Business", "Administrative", "Other"]
    )
)

# 4. Generate summaries per file
summaries_df = enriched_df.group_by("file_path").agg(
    fc.count("*").alias("segment_count"),
    semantic.reduce(
        "Create a concise summary of this conversation",
        fc.col("text"),
        order_by=fc.col("start_time")
    ).alias("summary")
)

# 5. Save results
enriched_df.write.save_as_table("processed_transcripts", mode="overwrite")
summaries_df.write.csv("transcript_summaries.csv")

# 6. Create MCP tools
from fenic.core.mcp.types import ToolParam
from fenic.core.types import StringType, IntegerType

session.catalog.create_tool(
    tool_name="query_transcripts",
    tool_description="Search and filter processed transcripts",
    tool_query=session.table("processed_transcripts").filter(
        fc.col("category") == fc.tool_param("category", StringType)
    ).limit(fc.tool_param("limit", IntegerType)),
    tool_params=[
        ToolParam(name="category", description="Filter by category", default_value="Technical"),
        ToolParam(name="limit", description="Max results", default_value=20)
    ]
)

# 7. Launch MCP server
from fenic.api.mcp import create_mcp_server, run_mcp_server_sync

server = create_mcp_server(
    session,
    "TranscriptAnalysis",
    user_defined_tools=session.catalog.list_tools()
)

run_mcp_server_sync(server, transport="http", port=8000)

This complete pipeline demonstrates the key patterns for production transcript processing: parsing, semantic enrichment, aggregation, persistence, and tool creation.

Best Practices for Transcript Processing

Strategic Model Selection

Use smaller models for simple tasks and reserve expensive models for complex reasoning:

python
# Classification with cheaper model
.with_column(
    "category",
    semantic.classify(fc.col("text"), categories, model_alias="mini")
)

# Complex extraction with more capable model
.with_column(
    "detailed_analysis",
    semantic.extract(fc.col("text"), ComplexSchema, model_alias="claude")
)

The cost difference between models can reach 100x. Strategic selection reduces expenses by 80% while maintaining quality for appropriate tasks.

Schema Design with Clear Descriptions

Field descriptions guide extraction accuracy:

python
class MeetingInsights(BaseModel):
    key_decisions: List[str] = Field(
        description="Major decisions made during the meeting"
    )
    action_items: List[str] = Field(
        description="Specific tasks assigned to team members with deadlines"
    )
    blockers: List[str] = Field(
        description="Obstacles or issues preventing progress"
    )
    participants: List[str] = Field(
        description="Full names of all meeting participants"
    )

Clear descriptions with examples and constraints improve results significantly while Literal types constrain outputs to valid categories.

Incremental Testing with Small Datasets

Validate logic and estimate costs before scaling:

python
# Development: 100 rows
df_sample = df.limit(100)
result = df_sample.with_column(
    "extracted",
    semantic.extract(fc.col("text"), schema)
).collect()

print(f"Cost for 100 rows: ${result.metrics().lm_metrics.total_cost}")

# Validate results, then scale
df_full.with_column(
    "extracted",
    semantic.extract(fc.col("text"), schema)
).write.parquet("output/")

Lazy evaluation and metrics enable cost estimation before processing millions of rows.

Hybrid Fuzzy and Semantic Matching

Use fuzzy matching for initial candidate selection before expensive semantic operations:

python
# Fast fuzzy scoring for blocking
candidates = (
    left_df.join(right_df)  # Cross join
    .with_column(
        "fuzzy_score",
        fc.text.compute_fuzzy_ratio(
            fc.col("company_name"),
            fc.col("business_name"),
            method="jaro_winkler"
        )
    )
    .filter(fc.col("fuzzy_score") > 80)
)

# Expensive semantic matching on candidates only
final = candidates.semantic.join(
    predicate="Are these the same company? Left: {{left_on}}, Right: {{right_on}}",
    left_on=fc.col("company_description"),
    right_on=fc.col("business_description")
)

This hybrid approach reduces costs by orders of magnitude compared to semantic joins on full cross-products.

Production Deployment Patterns

ASGI Server Integration

Deploy MCP servers in production environments:

python
from fenic.api.mcp import create_mcp_server, run_mcp_server_asgi

server = create_mcp_server(
    session,
    "TranscriptServer",
    user_defined_tools=session.catalog.list_tools()
)

app = run_mcp_server_asgi(
    server,
    stateless_http=True,
    path="/mcp"
)

# Launch with uvicorn
# uvicorn app:app --host 0.0.0.0 --port 8000 --workers 4

Error Handling Implementation

Handle failures gracefully in production pipelines:

python
# Add error columns
safe_df = segments_df.with_column(
    "classification",
    fc.when(
        fc.col("text").is_not_null() & (fc.length(fc.col("text")) > 0),
        semantic.classify(fc.col("text"), ["Type A", "Type B"])
    ).otherwise(fc.lit("unclassified"))
)

# Log failures
failed_df = segments_df.filter(
    fc.col("text").is_null()
).select(
    fc.col("file_path"),
    fc.lit("Null text content").alias("error")
)

failed_df.write.csv("processing_errors.csv")

Built-in retry logic and self-throttling handle transient failures automatically, but explicit error handling improves debugging and monitoring.

Integration with Existing Systems

Fenic's lakehouse-native architecture reads and writes standard formats without data movement:

python
# Read from existing data lake
df = session.read.parquet("s3://data-lake/raw/*.parquet")

# Process with semantic operators
processed = df.with_column(
    "extracted",
    semantic.extract(fc.col("content"), schema)
).with_column(
    "sentiment",
    semantic.analyze_sentiment(fc.col("content"))
)

# Write back to lake
processed.write.parquet("s3://data-lake/processed/")

Full compatibility with Parquet, Iceberg, Delta Lake, and Lance enables seamless integration with existing infrastructure. Built on Apache Arrow for ecosystem interoperability—processed data works with Spark, Polars, DuckDB, and pandas.

Conclusion

Processing audio transcripts for conversational AI insights requires infrastructure that handles format parsing, semantic understanding, and production reliability without brittle glue code. Fenic provides this foundation by treating inference as a first-class operation within a familiar DataFrame API.

The framework's semantic operators eliminate custom prompt engineering for common tasks while automatic batching, retry logic, and rate limiting ensure production stability. Combined with declarative tool creation through MCP, teams can build sophisticated transcript analysis pipelines that serve real-time agents with predictable performance.

How Typedef Cut RudderStack's Triage Time by 95% demonstrates these capabilities in production, processing thousands of support tickets and logs with semantic extraction and classification. Building agentic applications with declarative DataFrame APIs provides additional patterns for combining Fenic with agent frameworks.

Start with simple operations like semantic.classify or semantic.extract on small datasets, validate results and costs, then scale to production with confidence that the infrastructure handles optimization, error handling, and observability automatically.

Additional Resources: