allow using unconditional as prompts

bark/api.py

@@ -36,6 +36,7 @@ def semantic_to_waveform(
     history_prompt: Optional[str] = None,
     temp: float = 0.7,
     silent: bool = False,
+    output_full: bool = False,
 ):
     """Generate audio array from semantic input.
 
@@ -44,31 +45,49 @@ def semantic_to_waveform(
         history_prompt: history choice for audio cloning
         temp: generation temperature (1.0 more diverse, 0.0 more conservative)
         silent: disable progress bar
+        output_full: return full generation to be used as a history prompt
 
     Returns:
         numpy audio array at sample frequency 24khz
     """
-    x_coarse_gen = generate_coarse(
+    coarse_tokens = generate_coarse(
         semantic_tokens,
         history_prompt=history_prompt,
         temp=temp,
         silent=silent,
     )
-    x_fine_gen = generate_fine(
-        x_coarse_gen,
+    fine_tokens = generate_fine(
+        coarse_tokens,
         history_prompt=history_prompt,
         temp=0.5,
     )
-    audio_arr = codec_decode(x_fine_gen)
+    audio_arr = codec_decode(fine_tokens)
+    if output_full:
+        full_generation = {
+            "semantic_prompt": semantic_tokens,
+            "coarse_prompt": coarse_tokens,
+            "fine_prompt": fine_tokens,
+        }
+        return full_generation, audio_arr
     return audio_arr
 
 
+def save_as_prompt(filepath, full_generation):
+    assert(filepath.endswith(".npz"))
+    assert(isinstance(full_generation, dict))
+    assert("semantic_prompt" in full_generation)
+    assert("coarse_prompt" in full_generation)
+    assert("fine_prompt" in full_generation)
+    np.savez(filepath, **full_generation)
+
+
 def generate_audio(
     text: str,
     history_prompt: Optional[str] = None,
     text_temp: float = 0.7,
     waveform_temp: float = 0.7,
     silent: bool = False,
+    output_full: bool = False,
 ):
     """Generate audio array from input text.
 
@@ -78,14 +97,24 @@ def generate_audio(
         text_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
         waveform_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
         silent: disable progress bar
+        output_full: return full generation to be used as a history prompt
 
     Returns:
         numpy audio array at sample frequency 24khz
     """
-    x_semantic = text_to_semantic(
+    semantic_tokens = text_to_semantic(
         text, history_prompt=history_prompt, temp=text_temp, silent=silent,
     )
-    audio_arr = semantic_to_waveform(
-        x_semantic, history_prompt=history_prompt, temp=waveform_temp, silent=silent,
+    out = semantic_to_waveform(
+        semantic_tokens,
+        history_prompt=history_prompt,
+        temp=waveform_temp,
+        silent=silent,
+        output_full=output_full,
     )
+    if output_full:
+        full_generation, audio_arr = out
+        return full_generation, audio_arr
+    else:
+        audio_arr = out
     return audio_arr

bark/generation.py

@@ -365,10 +365,13 @@ def generate_text_semantic(
     text = _normalize_whitespace(text)
     assert len(text.strip()) > 0
     if history_prompt is not None:
-        assert (history_prompt in ALLOWED_PROMPTS)
-        semantic_history = np.load(
-            os.path.join(CUR_PATH, "assets", "prompts", f"{history_prompt}.npz")
-        )["semantic_prompt"]
+        if history_prompt.endswith(".npz"):
+            semantic_history = np.load(history_prompt)["semantic_prompt"]
+        else:
+            assert (history_prompt in ALLOWED_PROMPTS)
+            semantic_history = np.load(
+                os.path.join(CUR_PATH, "assets", "prompts", f"{history_prompt}.npz")
+            )["semantic_prompt"]
         assert (
             isinstance(semantic_history, np.ndarray)
             and len(semantic_history.shape) == 1
@@ -509,10 +512,13 @@ def generate_coarse(
     semantic_to_coarse_ratio = COARSE_RATE_HZ / SEMANTIC_RATE_HZ * N_COARSE_CODEBOOKS
     max_semantic_history = int(np.floor(max_coarse_history / semantic_to_coarse_ratio))
     if history_prompt is not None:
-        assert (history_prompt in ALLOWED_PROMPTS)
-        x_history = np.load(
-            os.path.join(CUR_PATH, "assets", "prompts", f"{history_prompt}.npz")
-        )
+        if history_prompt.endswith(".npz"):
+            x_history = np.load(history_prompt)
+        else:
+            assert (history_prompt in ALLOWED_PROMPTS)
+            x_history = np.load(
+                os.path.join(CUR_PATH, "assets", "prompts", f"{history_prompt}.npz")
+            )
         x_semantic_history = x_history["semantic_prompt"]
         x_coarse_history = x_history["coarse_prompt"]
         assert (
@@ -652,10 +658,13 @@ def generate_fine(
         and x_coarse_gen.max() <= CODEBOOK_SIZE - 1
     )
     if history_prompt is not None:
-        assert (history_prompt in ALLOWED_PROMPTS)
-        x_fine_history = np.load(
-            os.path.join(CUR_PATH, "assets", "prompts", f"{history_prompt}.npz")
-        )["fine_prompt"]
+        if history_prompt.endswith(".npz"):
+            x_fine_history = np.load(history_prompt)["fine_prompt"]
+        else:
+            assert (history_prompt in ALLOWED_PROMPTS)
+            x_fine_history = np.load(
+                os.path.join(CUR_PATH, "assets", "prompts", f"{history_prompt}.npz")
+            )["fine_prompt"]
         assert (
             isinstance(x_fine_history, np.ndarray)
             and len(x_fine_history.shape) == 2