fix(cost_calculator.py): support openai+azure tts calls

litellm/cost_calculator.py

@@ -13,6 +13,7 @@ from litellm.litellm_core_utils.llm_cost_calc.google import (
 from litellm.litellm_core_utils.llm_cost_calc.google import (
     cost_per_token as google_cost_per_token,
 )
+from litellm.litellm_core_utils.llm_cost_calc.utils import _generic_cost_per_character
 from litellm.utils import (
     CallTypes,
     CostPerToken,
@@ -62,6 +63,23 @@ def cost_per_token(
     ### CUSTOM PRICING ###
     custom_cost_per_token: Optional[CostPerToken] = None,
     custom_cost_per_second: Optional[float] = None,
+    ### CALL TYPE ###
+    call_type: Literal[
+        "embedding",
+        "aembedding",
+        "completion",
+        "acompletion",
+        "atext_completion",
+        "text_completion",
+        "image_generation",
+        "aimage_generation",
+        "moderation",
+        "amoderation",
+        "atranscription",
+        "transcription",
+        "aspeech",
+        "speech",
+    ] = "completion",
 ) -> Tuple[float, float]:
     """
     Calculates the cost per token for a given model, prompt tokens, and completion tokens.
@@ -76,6 +94,7 @@ def cost_per_token(
         custom_llm_provider (str): The llm provider to whom the call was made (see init.py for full list)
         custom_cost_per_token: Optional[CostPerToken]: the cost per input + output token for the llm api call.
         custom_cost_per_second: Optional[float]: the cost per second for the llm api call.
+        call_type: Optional[str]: the call type
 
     Returns:
         tuple: A tuple containing the cost in USD dollars for prompt tokens and completion tokens, respectively.
@@ -159,6 +178,27 @@ def cost_per_token(
             prompt_tokens=prompt_tokens,
             completion_tokens=completion_tokens,
         )
+    elif call_type == "speech" or call_type == "aspeech":
+        prompt_cost, completion_cost = _generic_cost_per_character(
+            model=model_without_prefix,
+            custom_llm_provider=custom_llm_provider,
+            prompt_characters=prompt_characters,
+            completion_characters=completion_characters,
+            custom_prompt_cost=None,
+            custom_completion_cost=0,
+        )
+        if prompt_cost is None or completion_cost is None:
+            raise ValueError(
+                "cost for tts call is None. prompt_cost={}, completion_cost={}, model={}, custom_llm_provider={}, prompt_characters={}, completion_characters={}".format(
+                    prompt_cost,
+                    completion_cost,
+                    model_without_prefix,
+                    custom_llm_provider,
+                    prompt_characters,
+                    completion_characters,
+                )
+            )
+        return prompt_cost, completion_cost
     elif model in model_cost_ref:
         print_verbose(f"Success: model={model} in model_cost_map")
         print_verbose(
@@ -289,7 +329,7 @@ def cost_per_token(
         return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
     else:
         # if model is not in model_prices_and_context_window.json. Raise an exception-let users know
-        error_str = f"Model not in model_prices_and_context_window.json. You passed model={model}. Register pricing for model - https://docs.litellm.ai/docs/proxy/custom_pricing\n"
+        error_str = f"Model not in model_prices_and_context_window.json. You passed model={model}, custom_llm_provider={custom_llm_provider}. Register pricing for model - https://docs.litellm.ai/docs/proxy/custom_pricing\n"
         raise litellm.exceptions.NotFoundError(  # type: ignore
             message=error_str,
             model=model,
@@ -535,6 +575,11 @@ def completion_cost(
                 raise Exception(
                     f"Model={image_gen_model_name} not found in completion cost model map"
                 )
+        elif (
+            call_type == CallTypes.speech.value or call_type == CallTypes.aspeech.value
+        ):
+            prompt_characters = litellm.utils._count_characters(text=prompt)
+
         # Calculate cost based on prompt_tokens, completion_tokens
         if (
             "togethercomputer" in model
@@ -591,6 +636,7 @@ def completion_cost(
             custom_cost_per_token=custom_cost_per_token,
             prompt_characters=prompt_characters,
             completion_characters=completion_characters,
+            call_type=call_type,
         )
         _final_cost = prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar
         print_verbose(

litellm/litellm_core_utils/llm_cost_calc/utils.py

@@ -0,0 +1,85 @@
+# What is this?
+## Helper utilities for cost_per_token()
+
+import traceback
+from typing import List, Literal, Optional, Tuple
+
+import litellm
+from litellm import verbose_logger
+
+
+def _generic_cost_per_character(
+    model: str,
+    custom_llm_provider: str,
+    prompt_characters: float,
+    completion_characters: float,
+    custom_prompt_cost: Optional[float],
+    custom_completion_cost: Optional[float],
+) -> Tuple[Optional[float], Optional[float]]:
+    """
+    Generic function to help calculate cost per character.
+    """
+    """
+    Calculates the cost per character for a given model, input messages, and response object.
+
+    Input:
+        - model: str, the model name without provider prefix
+        - custom_llm_provider: str, "vertex_ai-*"
+        - prompt_characters: float, the number of input characters
+        - completion_characters: float, the number of output characters
+
+    Returns:
+        Tuple[Optional[float], Optional[float]] - prompt_cost_in_usd, completion_cost_in_usd. 
+        - returns None if not able to calculate cost.
+
+    Raises:
+        Exception if 'input_cost_per_character' or 'output_cost_per_character' is missing from model_info
+    """
+    args = locals()
+    ## GET MODEL INFO
+    model_info = litellm.get_model_info(
+        model=model, custom_llm_provider=custom_llm_provider
+    )
+
+    ## CALCULATE INPUT COST
+    try:
+        if custom_prompt_cost is None:
+            assert (
+                "input_cost_per_character" in model_info
+                and model_info["input_cost_per_character"] is not None
+            ), "model info for model={} does not have 'input_cost_per_character'-pricing\nmodel_info={}".format(
+                model, model_info
+            )
+            custom_prompt_cost = model_info["input_cost_per_character"]
+
+        prompt_cost = prompt_characters * custom_prompt_cost
+    except Exception as e:
+        verbose_logger.error(
+            "litellm.litellm_core_utils.llm_cost_calc.utils.py::cost_per_character(): Exception occured - {}\n{}\nDefaulting to None".format(
+                str(e), traceback.format_exc()
+            )
+        )
+
+        prompt_cost = None
+
+    ## CALCULATE OUTPUT COST
+    try:
+        if custom_completion_cost is None:
+            assert (
+                "output_cost_per_character" in model_info
+                and model_info["output_cost_per_character"] is not None
+            ), "model info for model={} does not have 'output_cost_per_character'-pricing\nmodel_info={}".format(
+                model, model_info
+            )
+            custom_completion_cost = model_info["output_cost_per_character"]
+        completion_cost = completion_characters * custom_completion_cost
+    except Exception as e:
+        verbose_logger.error(
+            "litellm.litellm_core_utils.llm_cost_calc.utils.py::cost_per_character(): Exception occured - {}\n{}\nDefaulting to None".format(
+                str(e), traceback.format_exc()
+            )
+        )
+
+        completion_cost = None
+
+    return prompt_cost, completion_cost

litellm/model_prices_and_context_window_backup.json

@@ -398,6 +398,26 @@
         "output_cost_per_second": 0.0001, 
         "litellm_provider": "openai"
     },
+    "tts-1": {
+        "mode": "audio_speech", 
+        "input_cost_per_character": 0.000015,
+        "litellm_provider": "openai"
+    },
+    "tts-1-hd": {
+        "mode": "audio_speech", 
+        "input_cost_per_character": 0.000030,
+        "litellm_provider": "openai"
+    },
+    "azure/tts-1": {
+        "mode": "audio_speech", 
+        "input_cost_per_character": 0.000015,
+        "litellm_provider": "azure"
+    },
+    "azure/tts-1-hd": {
+        "mode": "audio_speech", 
+        "input_cost_per_character": 0.000030,
+        "litellm_provider": "azure"
+    },
     "azure/whisper-1": {
         "mode": "audio_transcription",
         "input_cost_per_second": 0, 

litellm/tests/test_completion_cost.py

@@ -712,7 +712,6 @@ def test_vertex_ai_claude_completion_cost():
     assert cost == predicted_cost
 
 
-
 @pytest.mark.parametrize("sync_mode", [True, False])
 @pytest.mark.asyncio
 async def test_completion_cost_hidden_params(sync_mode):
@@ -732,6 +731,7 @@ async def test_completion_cost_hidden_params(sync_mode):
     assert "response_cost" in response._hidden_params
     assert isinstance(response._hidden_params["response_cost"], float)
 
+
 def test_vertex_ai_gemini_predict_cost():
     model = "gemini-1.5-flash"
     messages = [{"role": "user", "content": "Hey, hows it going???"}]
@@ -739,3 +739,16 @@ def test_vertex_ai_gemini_predict_cost():
 
     assert predictive_cost > 0
 
+
+@pytest.mark.parametrize("model", ["openai/tts-1", "azure/tts-1"])
+def test_completion_cost_tts(model):
+    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
+    litellm.model_cost = litellm.get_model_cost_map(url="")
+
+    cost = completion_cost(
+        model=model,
+        prompt="the quick brown fox jumped over the lazy dogs",
+        call_type="speech",
+    )
+
+    assert cost > 0

litellm/utils.py

@@ -4705,7 +4705,9 @@ def get_model_info(model: str, custom_llm_provider: Optional[str] = None) -> Mod
             )
     except Exception:
         raise Exception(
-            "This model isn't mapped yet. Add it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json"
+            "This model isn't mapped yet. model={}, custom_llm_provider={}. Add it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json".format(
+                model, custom_llm_provider
+            )
         )
 
 

model_prices_and_context_window.json

@@ -398,6 +398,26 @@
         "output_cost_per_second": 0.0001, 
         "litellm_provider": "openai"
     },
+    "tts-1": {
+        "mode": "audio_speech", 
+        "input_cost_per_character": 0.000015,
+        "litellm_provider": "openai"
+    },
+    "tts-1-hd": {
+        "mode": "audio_speech", 
+        "input_cost_per_character": 0.000030,
+        "litellm_provider": "openai"
+    },
+    "azure/tts-1": {
+        "mode": "audio_speech", 
+        "input_cost_per_character": 0.000015,
+        "litellm_provider": "azure"
+    },
+    "azure/tts-1-hd": {
+        "mode": "audio_speech", 
+        "input_cost_per_character": 0.000030,
+        "litellm_provider": "azure"
+    },
     "azure/whisper-1": {
         "mode": "audio_transcription",
         "input_cost_per_second": 0,