Build VibeVoice Speech Pipelines in Colab
Run Microsoft VibeVoice's 7B ASR for speaker diarization and context-aware transcription plus 0.5B real-time TTS with 300ms latency using this Colab code—handles 60min audio and long-form synthesis.
Setup VibeVoice Environment for Instant ASR and TTS
Install via !pip install git+https://github.com/huggingface/transformers.git plus torch, gradio, and clone https://github.com/microsoft/VibeVoice. Restart runtime after editable install -e /content/VibeVoice. Load 7B ASR (microsoft/VibeVoice-ASR-HF, ~14GB download, float16 on auto device) and 0.5B TTS (microsoft/VibeVoice-Realtime-0.5B, set DDPM steps to 20). Use AutoProcessor for ASR inputs and VibeVoiceTextTokenizerFast for TTS. This enables 50+ languages, single-pass 60min transcription, and ~300ms streaming latency from ultra-low 7.5Hz tokenizers combining LLM context with diffusion audio gen.
Key transcribe(audio_path, context=None) wraps apply_transcription_request then generate and decode (formats: 'parsed', 'transcription_only'). For TTS, synthesize(text, voice="Grace", cfg_scale=3.0, steps=20) uses generate with return_speech=True, speaker_name, outputs 24kHz numpy audio—save via soundfile.
Unlock ASR Precision with Speakers, Context, and Batches
Achieve speaker diarization on podcasts: parsed output yields list of dicts with 'Speaker', 'Start/End' timestamps (s), 'Content'—e.g., Speaker 1 0.00s-5.23s: "Hello...". Context prompts fix hotwords: German sample mishears without context="About VibeVoice", correctly IDs "VibeVoice" with it. Batch multiple audios: apply_transcription_request(audio=[path1,path2], prompt=[ctx1,None]) generates all at once, decode to list of texts—scales for pipelines without loops.
Trade-offs: Long audio risks OOM; mitigate with acoustic_tokenizer_chunk_size=64000 in generate or bfloat16 dtype. Handles MP3/WAV/FLAC uploads via Colab files.
Craft Expressive TTS: Voices, CFG, and Long-Form Scaling
Four presets (Carter, Grace, Emma, Davis) yield distinct styles—compare same text across voices for prosody variety. CFG scale 1-5 controls adherence (3.0 default natural), steps 5-50 trade quality/speed (15 fast demo, 25 long-form). Generates 10min+ coherent speech: podcast script (~200 words) to 45s audio at cfg=3.5/steps=25. Next-token diffusion ensures pauses, intonation unlike rigid TTS.
Real-time viable: low-param model on CUDA/CPU. Gradio UI exposes text, voice dropdown, sliders for cfg/steps—gr.Interface(fn=tts_gradio) launches shareable demo.
Chain into Speech-to-Speech Pipelines with Optimizations
End-to-end: Transcribe input (transcribe(SAMPLE_GERMAN, context="About VibeVoice") → "Über VibeVoice..."), append response text, synthesize—yields conversational audio. Optimizations: torch.cuda.empty_cache(), gradient checkpointing, reduce steps to 10 for speed. Download outputs like /content/longform_output.wav. Responsible use: Research only, disclose AI speech, avoid impersonation.
Outcomes: Powers voice assistants, podcasts, accessibility—batch ASR cuts processing time, TTS enables interactive apps via Gradio.