Bump version to 1.1.0 and update benchmarks (#1161)

* update version

* Update CPU benchmarks

* Updated GPU benchmarks

* ..

* more gpu benchmarks

README.md

@@ -12,63 +12,53 @@ This implementation is up to 4 times faster than [openai/whisper](https://github
 
 For reference, here's the time and memory usage that are required to transcribe [**13 minutes**](https://www.youtube.com/watch?v=0u7tTptBo9I) of audio using different implementations:
 
-* [openai/whisper](https://github.com/openai/whisper)@[6dea21fd](https://github.com/openai/whisper/commit/6dea21fd7f7253bfe450f1e2512a0fe47ee2d258)
-* [whisper.cpp](https://github.com/ggerganov/whisper.cpp)@[3b010f9](https://github.com/ggerganov/whisper.cpp/commit/3b010f9bed9a6068609e9faf52383aea792b0362)
-* [faster-whisper](https://github.com/SYSTRAN/faster-whisper)@[cce6b53e](https://github.com/SYSTRAN/faster-whisper/commit/cce6b53e4554f71172dad188c45f10fb100f6e3e)
+* [openai/whisper](https://github.com/openai/whisper)@[v20240930](https://github.com/openai/whisper/tree/v20240930)
+* [whisper.cpp](https://github.com/ggerganov/whisper.cpp)@[v1.7.2](https://github.com/ggerganov/whisper.cpp/tree/v1.7.2)
+* [transformers](https://github.com/huggingface/transformers)@[v4.46.3](https://github.com/huggingface/transformers/tree/v4.46.3)
+* [faster-whisper](https://github.com/SYSTRAN/faster-whisper)@[v1.1.0](https://github.com/SYSTRAN/faster-whisper/tree/v1.1.0)
 
 ### Large-v2 model on GPU
 
-| Implementation | Precision | Beam size | Time | Max. GPU memory | Max. CPU memory |
-| --- | --- | --- | --- | --- | --- |
-| openai/whisper | fp16 | 5 | 4m30s | 11325MB | 9439MB |
-| faster-whisper | fp16 | 5 | 54s | 4755MB | 3244MB |
-| faster-whisper | int8 | 5 | 59s | 3091MB | 3117MB |
-
-*Executed with CUDA 11.7.1 on a NVIDIA Tesla V100S.*
+| Implementation | Precision | Beam size | Time | VRAM Usage |
+| --- | --- | --- | --- | --- |
+| openai/whisper | fp16 | 5 | 2m23s | 4708MB |
+| whisper.cpp (Flash Attention) | fp16 | 5 | 1m05s | 4127MB |
+| transformers (SDPA)[^1] | fp16 | 5 | 1m52s | 4960MB |
+| faster-whisper | fp16 | 5 | 1m03s | 4525MB |
+| faster-whisper (`batch_size=8`) | fp16 | 5 | 17s | 6090MB |
+| faster-whisper | int8 | 5 | 59s | 2926MB |
+| faster-whisper (`batch_size=8`) | int8 | 5 | 16s | 4500MB |
 
-### Small model on CPU
+### distil-whisper-large-v3 model on GPU
 
-| Implementation | Precision | Beam size | Time | Max. memory |
+| Implementation | Precision | Beam size | Time | YT Commons WER |
 | --- | --- | --- | --- | --- |
-| openai/whisper | fp32 | 5 | 10m31s | 3101MB |
-| whisper.cpp | fp32 | 5 | 17m42s | 1581MB |
-| whisper.cpp | fp16 | 5 | 12m39s | 873MB |
-| faster-whisper | fp32 | 5 | 2m44s | 1675MB |
-| faster-whisper | int8 | 5 | 2m04s | 995MB |
-
-*Executed with 8 threads on a Intel(R) Xeon(R) Gold 6226R.*
+| transformers (SDPA) (`batch_size=16`) | fp16 | 5 | 46m12s | 14.801 |
+| faster-whisper (`batch_size=16`) | fp16 | 5 | 25m50s | 13.527 |
 
+*GPU Benchmarks are Executed with CUDA 12.4 on a NVIDIA RTX 3070 Ti 8GB.*
+[^1]: transformers OOM for any batch size > 1
 
-### Distil-whisper
+### Small model on CPU
 
-| Implementation | Precision | Beam size | Time | Gigaspeech WER |
+| Implementation | Precision | Beam size | Time | RAM Usage |
 | --- | --- | --- | --- | --- |
-| distil-whisper/distil-large-v2 | fp16 | 4 |- | 10.36 |
-| [faster-distil-large-v2](https://huggingface.co/Systran/faster-distil-whisper-large-v2) | fp16 | 5 | - | 10.28 |
-| distil-whisper/distil-medium.en | fp16 | 4 | - | 11.21 |
-| [faster-distil-medium.en](https://huggingface.co/Systran/faster-distil-whisper-medium.en) | fp16 | 5 | - | 11.21 |
-
-*Executed with CUDA 11.4 on a NVIDIA 3090.*
-
-<details>
-<summary>testing details (click to expand)</summary>
+| openai/whisper | fp32 | 5 | 6m58s | 2335MB |
+| whisper.cpp | fp32 | 5 | 2m05s | 1049MB |
+| whisper.cpp (OpenVINO) | fp32 | 5 | 1m45s | 1642MB |
+| faster-whisper | fp32 | 5 | 2m37s | 2257MB |
+| faster-whisper (`batch_size=8`) | fp32 | 5 | 1m06s | 4230MB |
+| faster-whisper | int8 | 5 | 1m42s | 1477MB |
+| faster-whisper (`batch_size=8`) | int8 | 5 | 51s | 3608MB |
 
-For `distil-whisper/distil-large-v2`, the WER is tested with code sample from [link](https://huggingface.co/distil-whisper/distil-large-v2#evaluation). for `faster-distil-whisper`, the WER is tested with setting:
-```python
-from faster_whisper import WhisperModel
+*Executed with 8 threads on an Intel Core i7-12700K.*
 
-model_size = "distil-large-v2"
-# model_size = "distil-medium.en"
-# Run on GPU with FP16
-model = WhisperModel(model_size, device="cuda", compute_type="float16")
-segments, info = model.transcribe("audio.mp3", beam_size=5, language="en")
-```
-</details>
 
 ## Requirements
 
 * Python 3.8 or greater
 
+Unlike openai-whisper, FFmpeg does **not** need to be installed on the system. The audio is decoded with the Python library [PyAV](https://github.com/PyAV-Org/PyAV) which bundles the FFmpeg libraries in its package.
 
 ### GPU
 

faster_whisper/version.py

@@ -1,3 +1,3 @@
 """Version information."""
 
-__version__ = "1.1.0rc0"
+__version__ = "1.1.0"