Streaming output for the pytorch tts example. (#33)

* Streaming output for the pytorch tts example.

* Run the pre-commit hooks.

scripts/stt_from_file_with_prompt_pytorch.py

@@ -1,7 +1,6 @@
 """An example script that illustrates how one can prompt Kyutai STT models."""
 
 import argparse
-import dataclasses
 import itertools
 import math
 from collections import deque

scripts/tts_pytorch.py

@@ -11,20 +11,14 @@ import argparse
 import sys
 
 import numpy as np
+import queue
 import sphn
+import time
 import torch
 from moshi.models.loaders import CheckpointInfo
 from moshi.models.tts import DEFAULT_DSM_TTS_REPO, DEFAULT_DSM_TTS_VOICE_REPO, TTSModel
 
 
-def play_audio(audio: np.ndarray, sample_rate: int):
-    # Requires the Portaudio library which might not be available in all environments.
-    import sounddevice as sd
-
-    with sd.OutputStream(samplerate=sample_rate, blocksize=1920, channels=1):
-        sd.play(audio, sample_rate)
-
-
 def main():
     parser = argparse.ArgumentParser(
         description="Run Kyutai TTS using the PyTorch implementation"
@@ -57,6 +51,7 @@ def main():
     tts_model = TTSModel.from_checkpoint_info(
         checkpoint_info, n_q=32, temp=0.6, device=torch.device("cuda"), dtype=torch.half
     )
+    tts_model.mimi.streaming_forever(batch_size=1)
 
     if args.inp == "-":
         if sys.stdin.isatty():  # Interactive
@@ -75,22 +70,45 @@ def main():
         [voice_path], cfg_coef=2.0
     )
 
-    print("Generating audio...")
-    # This doesn't do streaming generation, but the model allows it. For now, see Rust
-    # example.
-    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)
-
     if args.out == "-":
-        print("Playing audio...")
+        # Stream the audio to the speakers using sounddevice.
-        play_audio(audios[0][0].cpu().numpy(), tts_model.mimi.sample_rate)
+        import sounddevice as sd
+
+        pcms = queue.Queue()
+
+        def _on_frame(frame):
+            if (frame != -1).all():
+                pcm = tts_model.mimi.decode(frame[:, 1:, :]).cpu().numpy()
+                pcms.put_nowait(np.clip(pcm[0, 0], -1, 1))
+
+        def audio_callback(outdata, _a, _b, _c):
+            try:
+                pcm_data = pcms.get(block=False)
+                outdata[:, 0] = pcm_data
+            except queue.Empty:
+                outdata[:] = 0
+
+        with sd.OutputStream(
+            samplerate=tts_model.mimi.sample_rate,
+            blocksize=1920,
+            channels=1,
+            callback=audio_callback,
+        ):
+            tts_model.generate([entries], [condition_attributes], on_frame=_on_frame)
+            time.sleep(3)
+            while True:
+                if pcms.qsize() == 0:
+                    break
+                time.sleep(1)
     else:
-        sphn.write_wav(args.out, audios[0].cpu().numpy(), tts_model.mimi.sample_rate)
+        result = tts_model.generate([entries], [condition_attributes])
-        print(f"Audio saved to {args.out}")
+        with torch.no_grad():
+            pcms = []
+            for frame in result.frames[tts_model.delay_steps :]:
+                pcm = tts_model.mimi.decode(frame[:, 1:, :]).cpu().numpy()
+                pcms.append(np.clip(pcm[0, 0], -1, 1))
+            pcm = np.concatenate(pcms, axis=-1)
+        sphn.write_wav(args.out, pcm, tts_model.mimi.sample_rate)
 
 
 if __name__ == "__main__":