Add tts_pytorch.py

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+  "python.analysis.typeCheckingMode": "standard"
+}

scripts/tts_pytorch.py

@@ -0,0 +1,81 @@
+# /// script
+# requires-python = ">=3.12"
+# dependencies = [
+#     "moshi @ git+https://git@github.com/kyutai-labs/moshi#egg=moshi&subdirectory=moshi",
+#     "torch",
+#     "sphn",
+# ]
+# ///
+import argparse
+import sys
+
+import sphn
+import torch
+from moshi.models.loaders import CheckpointInfo
+from moshi.models.tts import DEFAULT_DSM_TTS_REPO, DEFAULT_DSM_TTS_VOICE_REPO, TTSModel
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Run Kyutai TTS using the PyTorch implementation"
+    )
+    parser.add_argument("inp", type=str, help="Input file, use - for stdin.")
+    parser.add_argument(
+        "out", type=str, help="Output file to generate, use - for playing the audio"
+    )
+    parser.add_argument(
+        "--hf-repo",
+        type=str,
+        default=DEFAULT_DSM_TTS_REPO,
+        help="HF repo in which to look for the pretrained models.",
+    )
+    parser.add_argument(
+        "--voice-repo",
+        default=DEFAULT_DSM_TTS_VOICE_REPO,
+        help="HF repo in which to look for pre-computed voice embeddings.",
+    )
+    parser.add_argument(
+        "--voice",
+        default="expresso/ex03-ex01_happy_001_channel1_334s.wav",
+        help="The voice to use, relative to the voice repo root. "
+        f"See {DEFAULT_DSM_TTS_VOICE_REPO}",
+    )
+    args = parser.parse_args()
+
+    print("Loading model...")
+    checkpoint_info = CheckpointInfo.from_hf_repo(args.hf_repo)
+    tts_model = TTSModel.from_checkpoint_info(
+        checkpoint_info, n_q=32, temp=0.6, device=torch.device("cuda"), dtype=torch.half
+    )
+
+    if args.inp == "-":
+        print("Enter text to synthesize (Ctrl+D to end input):")
+        text = sys.stdin.read().strip()
+    else:
+        with open(args.inp, "r") as fobj:
+            text = fobj.read().strip()
+
+    # You could also generate multiple audios at once by passing a list of texts.
+    entries = tts_model.prepare_script([text], padding_between=1)
+    voice_path = tts_model.get_voice_path(args.voice)
+    # CFG coef goes here because the model was trained with CFG distillation,
+    # so it's not _actually_ doing CFG at inference time.
+    condition_attributes = tts_model.make_condition_attributes(
+        [voice_path], cfg_coef=2.0
+    )
+
+    print("Generating audio...")
+    # This doesn't do streaming generation,
+    result = tts_model.generate([entries], [condition_attributes])
+
+    frames = torch.cat(result.frames, dim=-1)
+    audio_tokens = frames[:, tts_model.lm.audio_offset :, tts_model.delay_steps :]
+    with torch.no_grad():
+        audios = tts_model.mimi.decode(audio_tokens)
+
+    sphn.write_wav(args.out, audios[0].cpu().numpy(), tts_model.mimi.sample_rate)
+    print(f"Audio saved to {args.out}")
+
+
+if __name__ == "__main__":
+    main()