Create README.md

README.md

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+---
+library_name: transformers
+pipeline_tag: audio-to-audio
+tags:
+- speech
+- audio
+- voice-conversion
+- speecht5
+- hifigan
+- speechbrain
+- xvector
+- sagemaker
+datasets:
+- cmu-arctic
+base_model:
+- microsoft/speecht5_vc
+- microsoft/speecht5_hifigan
+- speechbrain/spkrec-ecapa-voxceleb
+license: other
+language: en
+---
+
+# Model Card for `speech-conversion`
+
+> Any‑to‑any **voice conversion** (speech‑to‑speech) powered by Microsoft’s SpeechT5 voice‑conversion model. Convert a source utterance into the timbre of a target speaker using a short reference clip.
+
+This model card documents the repository **amirhossein-yousefi/speech-conversion**, which wraps the Hugging Face implementation of **SpeechT5 (voice conversion)** and the matching **HiFiGAN** vocoder, with a lightweight training loop and optional AWS SageMaker entry points.
+
+## Model Details
+
+### Model Description
+- **Developed by:** Amirhossein Yousefiramandi (repo author)
+- **Shared by :** Amirhossein Yousefiramandi
+- **Model type:** Speech-to-speech **voice conversion** using SpeechT5 (encoder–decoder) + HiFiGAN vocoder with **speaker x‑vectors** (ECAPA) for target voice conditioning
+- **Language(s):** English (training examples use CMU ARCTIC)
+- **License:** Repository does not include a license file (treat as “other”). Underlying base models on Hugging Face list **MIT** (see sources).
+- **Finetuned from model :** `microsoft/speecht5_vc` (with `microsoft/speecht5_hifigan` as vocoder); speaker embeddings via `speechbrain/spkrec-ecapa-voxceleb`
+
+### Model Sources
+- **Repository:** https://github.com/amirhossein-yousefi/speech-conversion
+- **Base model card:** https://huggingface.co/microsoft/speecht5_vc
+- **Paper (SpeechT5):** https://arxiv.org/abs/2110.07205
+
+## Uses
+
+### Direct Use
+- **Voice conversion (VC):** Given a **source** speech clip (content) and a short **reference** clip of the **target** speaker, synthesize the source content in the target’s voice. Intended for prototyping, research demos, and educational exploration.
+
+### Downstream Use 
+- **Fine‑tuning VC** on paired speakers (e.g., CMU ARCTIC pairs) for improved similarity.
+- Building **hosted inference** services (e.g., AWS SageMaker real‑time endpoint) using the provided handlers.
+
+### Out‑of‑Scope Use
+- Any application that **impersonates** individuals without explicit consent (e.g., fraud, deepfakes).
+- Safety‑critical domains where audio identity must not be spoofed (e.g., voice‑based authentication).
+
+## Bias, Risks, and Limitations
+- **Identity & consent:** Converting into a target speaker’s timbre can be misused for impersonation. Always obtain **informed consent** from target speakers.
+- **Dataset coverage:** CMU ARCTIC is studio‑quality North American English; performance may degrade on other accents, languages, or noisy conditions.
+- **Artifacts & intelligibility:** Conversion quality depends on model checkpoint quality and speaker embedding robustness; artifacts may appear for out‑of‑domain inputs or poor reference audio.
+
+### Recommendations
+- Keep reference audio **mono 16 kHz**, clean, and at least a few seconds long.
+- Use **GPU** for real‑time or faster‑than‑real‑time conversion.
+- Consider adding **post‑filters** (denoising, loudness normalization) for production use.
+- Obtain explicit **consent** and disclose synthesized audio where appropriate.
+
+## How to Get Started with the Model
+
+### CLI (local inference)
+```bash
+# Requirements (Python 3.10+)
+pip install "transformers>=4.42" "datasets>=2.20" "torch>=2.1" \
+            "numpy>=1.24" "sentencepiece>=0.1.99" "protobuf>=4.23" \
+            "speechbrain>=1.0.0" soundfile
+
+# One‑shot conversion (mono 16 kHz WAVs)
+python scripts/convert_once.py \
+  --checkpoint microsoft/speecht5_vc \
+  --src path/to/src.wav \
+  --ref path/to/ref.wav \
+  --out converted.wav