Duplicate from mlech26l/audio-function-calling

Co-authored-by: Mathias Lechner <mlech26l@users.noreply.huggingface.co>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bin filter=lfs diff=lfs merge=lfs -text
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+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
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+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
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+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: mit
+task_categories:
+  - text-generation
+  - audio-classification
+tags:
+  - function-calling
+  - tool-use
+  - synthetic
+  - audio
+  - multi-turn
+  - conversations
+language:
+  - en
+size_categories:
+  - n<1K
+---
+
+# Audio Function Calling Dataset
+
+A synthetic multi-turn conversation dataset for audio-based tool/function calling.
+Each sample contains a system prompt with tool definitions, alternating user and assistant turns,
+where user turns are designed for audio (natural spoken language with filler words) and assistant
+turns may include tool calls.
+
+> **Note:** This is the **first batch** (137 audio samples, 153 text-only samples). We are actively improving the generation pipeline and will be adding more data soon.
+
+## Structure
+
+| Subset | Samples | Description |
+|---|---|---|
+| `text_only/` | 153 | Text-only conversations (no audio) |
+| `audio/` | 137 | Conversations with TTS-generated audio for user turns |
+
+## Schema
+
+### text_only/conversations.parquet
+
+| Column | Type | Description |
+|---|---|---|
+| `sample_id` | string | Unique sample identifier |
+| `persona` | string | Description of the user persona (from [liquidrandom](https://github.com/mlech26l/liquidrandom)) |
+| `tools_json` | string | JSON array of tool definitions in OpenAI function calling format |
+| `conversation` | string | JSON array of conversation turns (`role`, `content`, optional `tool_calls`/`tool_call_id`) |
+| `num_turns` | int32 | Number of turns in the conversation |
+| `num_tool_calls` | int32 | Number of tool calls made by the assistant |
+
+### audio/conversations.parquet
+
+All columns from `text_only` plus:
+
+| Column | Type | Description |
+|---|---|---|
+| `voice` | string | TTS voice used for audio generation |
+| `user_audio` | list\<binary\> | WAV audio bytes for each user turn |
+
+## Generation Process
+
+1. **Seeding**: Each sample is seeded with a random persona and 1-3 tool groups from [liquidrandom](https://github.com/mlech26l/liquidrandom)
+2. **Conversation generation**: An LLM generates multi-turn conversations (2-5 turns) with tool calls, designed for spoken language
+3. **Quality validation**: A second LLM pass validates tool call correctness (no hallucinated arguments, matching tool definitions)
+4. **Audio synthesis**: User turns are converted to audio using TTS
+
+## Usage
+
+```python
+import pyarrow.parquet as pq
+
+# Load text-only conversations
+text = pq.read_table("text_only/conversations.parquet")
+
+# Load audio conversations
+audio = pq.read_table("audio/conversations.parquet")
+
+# Access a sample
+import json
+row = audio.to_pydict()
+conversation = json.loads(row["conversation"][0])
+tools = json.loads(row["tools_json"][0])
+user_audio_blobs = row["user_audio"][0]  # list of WAV bytes
+```
+
+## License
+
+MIT

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