Spaces:

mrfakename
/

E2-F5-TTS

Running on Zero

App Files Files Community

mrfakename commited on 13 days ago

Commit

4115638

verified ·

1 Parent(s): 1674828

Sync from GitHub repo

Browse files

This Space is synced from the GitHub repo: https://github.com/SWivid/F5-TTS. Please submit contributions to the Space there

Files changed (5) hide show

README_REPO.md +15 -3
pyproject.toml +1 -1
src/f5_tts/model/cfm.py +1 -1
src/f5_tts/runtime/triton_trtllm/README.md +6 -5
src/f5_tts/runtime/triton_trtllm/patch/__init__.py +137 -135

README_REPO.md CHANGED Viewed

@@ -110,6 +110,9 @@ docker container run --rm -it --gpus=all --mount 'type=volume,source=f5-tts,targ
 ## Inference
 ### 1. Gradio App
 Currently supported features:
@@ -176,10 +179,18 @@ f5-tts_infer-cli -c custom.toml
 f5-tts_infer-cli -c src/f5_tts/infer/examples/multi/story.toml
 ```
-### 3. More instructions
-- In order to have better generation results, take a moment to read [detailed guidance](src/f5_tts/infer).
-- The [Issues](https://github.com/SWivid/F5-TTS/issues?q=is%3Aissue) are very useful, please try to find the solution by properly searching the keywords of problem encountered. If no answer found, then feel free to open an issue.
 ## Training
@@ -231,6 +242,7 @@ Note: Some model components have linting exceptions for E722 to accommodate tens
 - [mrfakename](https://x.com/realmrfakename) huggingface space demo ~
 - [f5-tts-mlx](https://github.com/lucasnewman/f5-tts-mlx/tree/main) Implementation with MLX framework by [Lucas Newman](https://github.com/lucasnewman)
 - [F5-TTS-ONNX](https://github.com/DakeQQ/F5-TTS-ONNX) ONNX Runtime version by [DakeQQ](https://github.com/DakeQQ)
 ## Citation
 If our work and codebase is useful for you, please cite as:

 ## Inference
+- In order to achieve desired performance, take a moment to read [detailed guidance](src/f5_tts/infer).
+- By properly searching the keywords of problem encountered, [issues](https://github.com/SWivid/F5-TTS/issues?q=is%3Aissue) are very helpful.
 ### 1. Gradio App
 Currently supported features:
 f5-tts_infer-cli -c src/f5_tts/infer/examples/multi/story.toml
 ```
+### 3. Runtime
+Deployment solution with Triton and TensorRT-LLM.
+#### Benchmark Results
+Decoding on a single L20 GPU, using 26 different prompt_audio & target_text pairs.
+| Model | Concurrency | Avg Latency    | RTF   |
+|-------|-------------|----------------|-------|
+| F5-TTS Base (Vocos) | 1     | 253 ms | 0.0394|
+See [detailed instructions](src\f5_tts\runtime\triton_trtllm\README.md) for more information.
 ## Training
 - [mrfakename](https://x.com/realmrfakename) huggingface space demo ~
 - [f5-tts-mlx](https://github.com/lucasnewman/f5-tts-mlx/tree/main) Implementation with MLX framework by [Lucas Newman](https://github.com/lucasnewman)
 - [F5-TTS-ONNX](https://github.com/DakeQQ/F5-TTS-ONNX) ONNX Runtime version by [DakeQQ](https://github.com/DakeQQ)
+- [Yuekai Zhang](https://github.com/yuekaizhang) Triton and TensorRT-LLM support ~
 ## Citation
 If our work and codebase is useful for you, please cite as:

pyproject.toml CHANGED Viewed

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "f5-tts"
-version = "1.0.10"
 description = "F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching"
 readme = "README.md"
 license = {text = "MIT License"}

 [project]
 name = "f5-tts"
+version = "1.1.0"
 description = "F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching"
 readme = "README.md"
 license = {text = "MIT License"}

src/f5_tts/model/cfm.py CHANGED Viewed

@@ -270,7 +270,7 @@ class CFM(nn.Module):
         else:
             drop_text = False
-        # if want rigourously mask out padding, record in collate_fn in dataset.py, and pass in here
         # adding mask will use more memory, thus also need to adjust batchsampler with scaled down threshold for long sequences
         pred = self.transformer(
             x=φ, cond=cond, text=text, time=time, drop_audio_cond=drop_audio_cond, drop_text=drop_text

         else:
             drop_text = False
+        # if want rigorously mask out padding, record in collate_fn in dataset.py, and pass in here
         # adding mask will use more memory, thus also need to adjust batchsampler with scaled down threshold for long sequences
         pred = self.transformer(
             x=φ, cond=cond, text=text, time=time, drop_audio_cond=drop_audio_cond, drop_text=drop_text

src/f5_tts/runtime/triton_trtllm/README.md CHANGED Viewed

@@ -1,4 +1,4 @@
-## Triton Inference Serving Best Practice for F5 TTS
 ### Quick Start
 Directly launch the service using docker compose.
@@ -21,14 +21,15 @@ docker run -it --name "f5-server" --gpus all --net host -v $your_mount_dir --shm
 ### Export Models to TensorRT-LLM and Launch Server
 Inside docker container, we would follow the official guide of TensorRT-LLM to build qwen and whisper TensorRT-LLM engines. See [here](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/whisper).
 ```sh
 bash run.sh 0 4 F5TTS_Base
 ```
 ### HTTP Client
 ```sh
 python3 client_http.py
 ```
 ### Benchmark using Dataset
 ```sh
 num_task=2
@@ -38,9 +39,9 @@ python3 client_grpc.py --num-tasks $num_task --huggingface-dataset yuekai/seed_t
 ### Benchmark Results
 Decoding on a single L20 GPU, using 26 different prompt_audio/target_text pairs.
-| Model | Concurrency | Avg Latency     | RTF |
-|-------|-------------|-----------------|--|
 | F5-TTS Base (Vocos) | 1     | 253 ms | 0.0394|
 ### Credits
-1. [F5-TTS-TRTLLM](https://github.com/Bigfishering/f5-tts-trtllm)

+## Triton Inference Serving Best Practice for F5-TTS
 ### Quick Start
 Directly launch the service using docker compose.
 ### Export Models to TensorRT-LLM and Launch Server
 Inside docker container, we would follow the official guide of TensorRT-LLM to build qwen and whisper TensorRT-LLM engines. See [here](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/whisper).
 ```sh
 bash run.sh 0 4 F5TTS_Base
 ```
 ### HTTP Client
 ```sh
 python3 client_http.py
 ```
 ### Benchmark using Dataset
 ```sh
 num_task=2
 ### Benchmark Results
 Decoding on a single L20 GPU, using 26 different prompt_audio/target_text pairs.
+| Model | Concurrency | Avg Latency    | RTF   |
+|-------|-------------|----------------|-------|
 | F5-TTS Base (Vocos) | 1     | 253 ms | 0.0394|
 ### Credits
+1. [F5-TTS-TRTLLM](https://github.com/Bigfishering/f5-tts-trtllm)

src/f5_tts/runtime/triton_trtllm/patch/__init__.py CHANGED Viewed

@@ -13,10 +13,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from .baichuan.model import BaichuanForCausalLM
-from .bert.model import (BertForQuestionAnswering,
-                         BertForSequenceClassification, BertModel,
-                         RobertaForQuestionAnswering,
-                         RobertaForSequenceClassification, RobertaModel)
 from .bloom.model import BloomForCausalLM, BloomModel
 from .chatglm.config import ChatGLMConfig
 from .chatglm.model import ChatGLMForCausalLM, ChatGLMModel
@@ -46,8 +50,7 @@ from .mamba.model import MambaForCausalLM
 from .medusa.config import MedusaConfig
 from .medusa.model import MedusaForCausalLm
 from .mllama.model import MLLaMAModel
-from .modeling_utils import (PretrainedConfig, PretrainedModel,
-                             SpeculativeDecodingMode)
 from .mpt.model import MPTForCausalLM, MPTModel
 from .nemotron_nas.model import DeciLMForCausalLM
 from .opt.model import OPTForCausalLM, OPTModel
@@ -59,138 +62,137 @@ from .redrafter.model import ReDrafterForCausalLM
 from .f5tts.model import F5TTS
 __all__ = [
-    'BertModel',
-    'BertForQuestionAnswering',
-    'BertForSequenceClassification',
-    'RobertaModel',
-    'RobertaForQuestionAnswering',
-    'RobertaForSequenceClassification',
-    'BloomModel',
-    'BloomForCausalLM',
-    'DiT',
-    'DeepseekForCausalLM',
-    'FalconConfig',
-    'DeepseekV2ForCausalLM',
-    'FalconForCausalLM',
-    'FalconModel',
-    'GPTConfig',
-    'GPTModel',
-    'GPTForCausalLM',
-    'OPTForCausalLM',
-    'OPTModel',
-    'LLaMAConfig',
-    'LLaMAForCausalLM',
-    'LLaMAModel',
-    'MedusaConfig',
-    'MedusaForCausalLm',
-    'ReDrafterForCausalLM',
-    'GPTJConfig',
-    'GPTJModel',
-    'GPTJForCausalLM',
-    'GPTNeoXModel',
-    'GPTNeoXForCausalLM',
-    'PhiModel',
-    'PhiConfig',
-    'Phi3Model',
-    'Phi3Config',
-    'PhiForCausalLM',
-    'Phi3ForCausalLM',
-    'ChatGLMConfig',
-    'ChatGLMForCausalLM',
-    'ChatGLMModel',
-    'BaichuanForCausalLM',
-    'QWenConfig'
-    'QWenForCausalLM',
-    'QWenModel',
-    'EncoderModel',
-    'DecoderModel',
-    'PretrainedConfig',
-    'PretrainedModel',
-    'WhisperEncoder',
-    'MambaForCausalLM',
-    'MambaConfig',
-    'MPTForCausalLM',
-    'MPTModel',
-    'SkyworkForCausalLM',
-    'GemmaConfig',
-    'GemmaForCausalLM',
-    'DbrxConfig',
-    'DbrxForCausalLM',
-    'RecurrentGemmaForCausalLM',
-    'CogVLMConfig',
-    'CogVLMForCausalLM',
-    'EagleForCausalLM',
-    'SpeculativeDecodingMode',
-    'CohereForCausalLM',
-    'MLLaMAModel',
-    'F5TTS',
 ]
 MODEL_MAP = {
-    'GPT2LMHeadModel': GPTForCausalLM,
-    'GPT2LMHeadCustomModel': GPTForCausalLM,
-    'GPTBigCodeForCausalLM': GPTForCausalLM,
-    'Starcoder2ForCausalLM': GPTForCausalLM,
-    'FuyuForCausalLM': GPTForCausalLM,
-    'Kosmos2ForConditionalGeneration': GPTForCausalLM,
-    'JAISLMHeadModel': GPTForCausalLM,
-    'GPTForCausalLM': GPTForCausalLM,
-    'NemotronForCausalLM': GPTForCausalLM,
-    'OPTForCausalLM': OPTForCausalLM,
-    'BloomForCausalLM': BloomForCausalLM,
-    'RWForCausalLM': FalconForCausalLM,
-    'FalconForCausalLM': FalconForCausalLM,
-    'PhiForCausalLM': PhiForCausalLM,
-    'Phi3ForCausalLM': Phi3ForCausalLM,
-    'Phi3VForCausalLM': Phi3ForCausalLM,
-    'Phi3SmallForCausalLM': Phi3ForCausalLM,
-    'PhiMoEForCausalLM': Phi3ForCausalLM,
-    'MambaForCausalLM': MambaForCausalLM,
-    'GPTNeoXForCausalLM': GPTNeoXForCausalLM,
-    'GPTJForCausalLM': GPTJForCausalLM,
-    'MPTForCausalLM': MPTForCausalLM,
-    'GLMModel': ChatGLMForCausalLM,
-    'ChatGLMModel': ChatGLMForCausalLM,
-    'ChatGLMForCausalLM': ChatGLMForCausalLM,
-    'LlamaForCausalLM': LLaMAForCausalLM,
-    'ExaoneForCausalLM': LLaMAForCausalLM,
-    'MistralForCausalLM': LLaMAForCausalLM,
-    'MixtralForCausalLM': LLaMAForCausalLM,
-    'ArcticForCausalLM': LLaMAForCausalLM,
-    'Grok1ModelForCausalLM': GrokForCausalLM,
-    'InternLMForCausalLM': LLaMAForCausalLM,
-    'InternLM2ForCausalLM': LLaMAForCausalLM,
-    'MedusaForCausalLM': MedusaForCausalLm,
-    'ReDrafterForCausalLM': ReDrafterForCausalLM,
-    'BaichuanForCausalLM': BaichuanForCausalLM,
-    'BaiChuanForCausalLM': BaichuanForCausalLM,
-    'SkyworkForCausalLM': LLaMAForCausalLM,
     GEMMA_ARCHITECTURE: GemmaForCausalLM,
     GEMMA2_ARCHITECTURE: GemmaForCausalLM,
-    'QWenLMHeadModel': QWenForCausalLM,
-    'QWenForCausalLM': QWenForCausalLM,
-    'Qwen2ForCausalLM': QWenForCausalLM,
-    'Qwen2MoeForCausalLM': QWenForCausalLM,
-    'Qwen2ForSequenceClassification': QWenForCausalLM,
-    'Qwen2VLForConditionalGeneration': QWenForCausalLM,
-    'WhisperEncoder': WhisperEncoder,
-    'EncoderModel': EncoderModel,
-    'DecoderModel': DecoderModel,
-    'DbrxForCausalLM': DbrxForCausalLM,
-    'RecurrentGemmaForCausalLM': RecurrentGemmaForCausalLM,
-    'CogVLMForCausalLM': CogVLMForCausalLM,
-    'DiT': DiT,
-    'DeepseekForCausalLM': DeepseekForCausalLM,
-    'DeciLMForCausalLM': DeciLMForCausalLM,
-    'DeepseekV2ForCausalLM': DeepseekV2ForCausalLM,
-    'EagleForCausalLM': EagleForCausalLM,
-    'CohereForCausalLM': CohereForCausalLM,
-    'MllamaForConditionalGeneration': MLLaMAModel,
-    'BertForQuestionAnswering': BertForQuestionAnswering,
-    'BertForSequenceClassification': BertForSequenceClassification,
-    'BertModel': BertModel,
-    'RobertaModel': RobertaModel,
-    'RobertaForQuestionAnswering': RobertaForQuestionAnswering,
-    'RobertaForSequenceClassification': RobertaForSequenceClassification,
-    'F5TTS': F5TTS
 }

 # See the License for the specific language governing permissions and
 # limitations under the License.
 from .baichuan.model import BaichuanForCausalLM
+from .bert.model import (
+    BertForQuestionAnswering,
+    BertForSequenceClassification,
+    BertModel,
+    RobertaForQuestionAnswering,
+    RobertaForSequenceClassification,
+    RobertaModel,
+)
 from .bloom.model import BloomForCausalLM, BloomModel
 from .chatglm.config import ChatGLMConfig
 from .chatglm.model import ChatGLMForCausalLM, ChatGLMModel
 from .medusa.config import MedusaConfig
 from .medusa.model import MedusaForCausalLm
 from .mllama.model import MLLaMAModel
+from .modeling_utils import PretrainedConfig, PretrainedModel, SpeculativeDecodingMode
 from .mpt.model import MPTForCausalLM, MPTModel
 from .nemotron_nas.model import DeciLMForCausalLM
 from .opt.model import OPTForCausalLM, OPTModel
 from .f5tts.model import F5TTS
 __all__ = [
+    "BertModel",
+    "BertForQuestionAnswering",
+    "BertForSequenceClassification",
+    "RobertaModel",
+    "RobertaForQuestionAnswering",
+    "RobertaForSequenceClassification",
+    "BloomModel",
+    "BloomForCausalLM",
+    "DiT",
+    "DeepseekForCausalLM",
+    "FalconConfig",
+    "DeepseekV2ForCausalLM",
+    "FalconForCausalLM",
+    "FalconModel",
+    "GPTConfig",
+    "GPTModel",
+    "GPTForCausalLM",
+    "OPTForCausalLM",
+    "OPTModel",
+    "LLaMAConfig",
+    "LLaMAForCausalLM",
+    "LLaMAModel",
+    "MedusaConfig",
+    "MedusaForCausalLm",
+    "ReDrafterForCausalLM",
+    "GPTJConfig",
+    "GPTJModel",
+    "GPTJForCausalLM",
+    "GPTNeoXModel",
+    "GPTNeoXForCausalLM",
+    "PhiModel",
+    "PhiConfig",
+    "Phi3Model",
+    "Phi3Config",
+    "PhiForCausalLM",
+    "Phi3ForCausalLM",
+    "ChatGLMConfig",
+    "ChatGLMForCausalLM",
+    "ChatGLMModel",
+    "BaichuanForCausalLM",
+    "QWenConfigQWenForCausalLM",
+    "QWenModel",
+    "EncoderModel",
+    "DecoderModel",
+    "PretrainedConfig",
+    "PretrainedModel",
+    "WhisperEncoder",
+    "MambaForCausalLM",
+    "MambaConfig",
+    "MPTForCausalLM",
+    "MPTModel",
+    "SkyworkForCausalLM",
+    "GemmaConfig",
+    "GemmaForCausalLM",
+    "DbrxConfig",
+    "DbrxForCausalLM",
+    "RecurrentGemmaForCausalLM",
+    "CogVLMConfig",
+    "CogVLMForCausalLM",
+    "EagleForCausalLM",
+    "SpeculativeDecodingMode",
+    "CohereForCausalLM",
+    "MLLaMAModel",
+    "F5TTS",
 ]
 MODEL_MAP = {
+    "GPT2LMHeadModel": GPTForCausalLM,
+    "GPT2LMHeadCustomModel": GPTForCausalLM,
+    "GPTBigCodeForCausalLM": GPTForCausalLM,
+    "Starcoder2ForCausalLM": GPTForCausalLM,
+    "FuyuForCausalLM": GPTForCausalLM,
+    "Kosmos2ForConditionalGeneration": GPTForCausalLM,
+    "JAISLMHeadModel": GPTForCausalLM,
+    "GPTForCausalLM": GPTForCausalLM,
+    "NemotronForCausalLM": GPTForCausalLM,
+    "OPTForCausalLM": OPTForCausalLM,
+    "BloomForCausalLM": BloomForCausalLM,
+    "RWForCausalLM": FalconForCausalLM,
+    "FalconForCausalLM": FalconForCausalLM,
+    "PhiForCausalLM": PhiForCausalLM,
+    "Phi3ForCausalLM": Phi3ForCausalLM,
+    "Phi3VForCausalLM": Phi3ForCausalLM,
+    "Phi3SmallForCausalLM": Phi3ForCausalLM,
+    "PhiMoEForCausalLM": Phi3ForCausalLM,
+    "MambaForCausalLM": MambaForCausalLM,
+    "GPTNeoXForCausalLM": GPTNeoXForCausalLM,
+    "GPTJForCausalLM": GPTJForCausalLM,
+    "MPTForCausalLM": MPTForCausalLM,
+    "GLMModel": ChatGLMForCausalLM,
+    "ChatGLMModel": ChatGLMForCausalLM,
+    "ChatGLMForCausalLM": ChatGLMForCausalLM,
+    "LlamaForCausalLM": LLaMAForCausalLM,
+    "ExaoneForCausalLM": LLaMAForCausalLM,
+    "MistralForCausalLM": LLaMAForCausalLM,
+    "MixtralForCausalLM": LLaMAForCausalLM,
+    "ArcticForCausalLM": LLaMAForCausalLM,
+    "Grok1ModelForCausalLM": GrokForCausalLM,
+    "InternLMForCausalLM": LLaMAForCausalLM,
+    "InternLM2ForCausalLM": LLaMAForCausalLM,
+    "MedusaForCausalLM": MedusaForCausalLm,
+    "ReDrafterForCausalLM": ReDrafterForCausalLM,
+    "BaichuanForCausalLM": BaichuanForCausalLM,
+    "BaiChuanForCausalLM": BaichuanForCausalLM,
+    "SkyworkForCausalLM": LLaMAForCausalLM,
     GEMMA_ARCHITECTURE: GemmaForCausalLM,
     GEMMA2_ARCHITECTURE: GemmaForCausalLM,
+    "QWenLMHeadModel": QWenForCausalLM,
+    "QWenForCausalLM": QWenForCausalLM,
+    "Qwen2ForCausalLM": QWenForCausalLM,
+    "Qwen2MoeForCausalLM": QWenForCausalLM,
+    "Qwen2ForSequenceClassification": QWenForCausalLM,
+    "Qwen2VLForConditionalGeneration": QWenForCausalLM,
+    "WhisperEncoder": WhisperEncoder,
+    "EncoderModel": EncoderModel,
+    "DecoderModel": DecoderModel,
+    "DbrxForCausalLM": DbrxForCausalLM,
+    "RecurrentGemmaForCausalLM": RecurrentGemmaForCausalLM,
+    "CogVLMForCausalLM": CogVLMForCausalLM,
+    "DiT": DiT,
+    "DeepseekForCausalLM": DeepseekForCausalLM,
+    "DeciLMForCausalLM": DeciLMForCausalLM,
+    "DeepseekV2ForCausalLM": DeepseekV2ForCausalLM,
+    "EagleForCausalLM": EagleForCausalLM,
+    "CohereForCausalLM": CohereForCausalLM,
+    "MllamaForConditionalGeneration": MLLaMAModel,
+    "BertForQuestionAnswering": BertForQuestionAnswering,
+    "BertForSequenceClassification": BertForSequenceClassification,
+    "BertModel": BertModel,
+    "RobertaModel": RobertaModel,
+    "RobertaForQuestionAnswering": RobertaForQuestionAnswering,
+    "RobertaForSequenceClassification": RobertaForSequenceClassification,
+    "F5TTS": F5TTS,
 }