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Rename FlaxWhisperPipline -> FlaxWhisperPipeline #114

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Rename FlaxWhisperPipline -> FlaxWhisperPipeline #114

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28 changes: 14 additions & 14 deletions README.md
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Expand Up @@ -32,17 +32,17 @@ pip install --upgrade --no-deps --force-reinstall git+https://github.com/sanchit

## Pipeline Usage

The recommended way of running Whisper JAX is through the [`FlaxWhisperPipline`](https://github.com/sanchit-gandhi/whisper-jax/blob/main/whisper_jax/pipeline.py#L57) abstraction class. This class handles all
The recommended way of running Whisper JAX is through the [`FlaxWhisperPipeline`](https://github.com/sanchit-gandhi/whisper-jax/blob/main/whisper_jax/pipeline.py#L57) abstraction class. This class handles all
the necessary pre- and post-processing, as well as wrapping the generate method for data parallelism across accelerator devices.

Whisper JAX makes use of JAX's [`pmap`](https://jax.readthedocs.io/en/latest/_autosummary/jax.pmap.html) function for data parallelism across GPU/TPU devices. This function is _Just In Time (JIT)_
compiled the first time it is called. Thereafter, the function will be _cached_, enabling it to be run in super-fast time:

```python
from whisper_jax import FlaxWhisperPipline
from whisper_jax import FlaxWhisperPipeline

# instantiate pipeline
pipeline = FlaxWhisperPipline("openai/whisper-large-v2")
pipeline = FlaxWhisperPipeline("openai/whisper-large-v2")

# JIT compile the forward call - slow, but we only do once
text = pipeline("audio.mp3")
Expand All @@ -59,11 +59,11 @@ of the model weights.

For most GPUs, the dtype should be set to `jnp.float16`. For A100 GPUs or TPUs, the dtype should be set to `jnp.bfloat16`:
```python
from whisper_jax import FlaxWhisperPipline
from whisper_jax import FlaxWhisperPipeline
import jax.numpy as jnp

# instantiate pipeline in bfloat16
pipeline = FlaxWhisperPipline("openai/whisper-large-v2", dtype=jnp.bfloat16)
pipeline = FlaxWhisperPipeline("openai/whisper-large-v2", dtype=jnp.bfloat16)
```

### Batching
Expand All @@ -75,10 +75,10 @@ provides a 10x speed-up compared to transcribing the audio samples sequentially,
To enable batching, pass the `batch_size` parameter when you instantiate the pipeline:

```python
from whisper_jax import FlaxWhisperPipline
from whisper_jax import FlaxWhisperPipeline

# instantiate pipeline with batching
pipeline = FlaxWhisperPipline("openai/whisper-large-v2", batch_size=16)
pipeline = FlaxWhisperPipeline("openai/whisper-large-v2", batch_size=16)
```

### Task
Expand All @@ -93,7 +93,7 @@ text = pipeline("audio.mp3", task="translate")

### Timestamps

The [`FlaxWhisperPipline`](https://github.com/sanchit-gandhi/whisper-jax/blob/main/whisper_jax/pipeline.py#L57) also supports timestamp prediction. Note that enabling timestamps will require a second JIT compilation of the
The [`FlaxWhisperPipeline`](https://github.com/sanchit-gandhi/whisper-jax/blob/main/whisper_jax/pipeline.py#L57) also supports timestamp prediction. Note that enabling timestamps will require a second JIT compilation of the
forward call, this time including the timestamp outputs:

```python
Expand All @@ -108,11 +108,11 @@ In the following code snippet, we instantiate the model in bfloat16 precision wi
returning timestamps tokens:

```python
from whisper_jax import FlaxWhisperPipline
from whisper_jax import FlaxWhisperPipeline
import jax.numpy as jnp

# instantiate pipeline with bfloat16 and enable batching
pipeline = FlaxWhisperPipline("openai/whisper-large-v2", dtype=jnp.bfloat16, batch_size=16)
pipeline = FlaxWhisperPipeline("openai/whisper-large-v2", dtype=jnp.bfloat16, batch_size=16)

# transcribe and return timestamps
outputs = pipeline("audio.mp3", task="transcribe", return_timestamps=True)
Expand Down Expand Up @@ -188,7 +188,7 @@ the next time they are required. Note that converting weights from PyTorch to Fl

For example, to convert the fine-tuned checkpoint [`sanchit-gandhi/whisper-small-hi`](https://huggingface.co/sanchit-gandhi/whisper-small-hi) from the blog post [Fine-Tuning Whisper](https://huggingface.co/blog/fine-tune-whisper):
```python
from whisper_jax import FlaxWhisperForConditionalGeneration, FlaxWhisperPipline
from whisper_jax import FlaxWhisperForConditionalGeneration, FlaxWhisperPipeline
import jax.numpy as jnp

checkpoint_id = "sanchit-gandhi/whisper-small-hi"
Expand All @@ -198,7 +198,7 @@ model = FlaxWhisperForConditionalGeneration.from_pretrained(checkpoint_id, from_
model.push_to_hub(checkpoint_id)

# now we can load the Flax weights directly as required
pipeline = FlaxWhisperPipline(checkpoint_id, dtype=jnp.bfloat16, batch_size=16)
pipeline = FlaxWhisperPipeline(checkpoint_id, dtype=jnp.bfloat16, batch_size=16)
```

## Advanced Usage
Expand All @@ -212,7 +212,7 @@ The following code snippet demonstrates how data parallelism can be achieved usi
an entirely equivalent way to `pmap`:

```python
from whisper_jax import FlaxWhisperPipline
from whisper_jax import FlaxWhisperPipeline
import jax.numpy as jnp

# 2D parameter and activation partitioning for DP
Expand All @@ -230,7 +230,7 @@ logical_axis_rules_dp = (
("channels", None),
)

pipeline = FlaxWhisperPipline("openai/whisper-large-v2", dtype=jnp.bfloat16, batch_size=16)
pipeline = FlaxWhisperPipeline("openai/whisper-large-v2", dtype=jnp.bfloat16, batch_size=16)
pipeline.shard_params(num_mp_partitions=1, logical_axis_rules=logical_axis_rules_dp)
```

Expand Down
4 changes: 2 additions & 2 deletions app/app.py
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Expand Up @@ -13,7 +13,7 @@
from transformers.models.whisper.tokenization_whisper import TO_LANGUAGE_CODE
from transformers.pipelines.audio_utils import ffmpeg_read

from whisper_jax import FlaxWhisperPipline
from whisper_jax import FlaxWhisperPipeline


cc.initialize_cache("./jax_cache")
Expand Down Expand Up @@ -73,7 +73,7 @@ def format_timestamp(seconds: float, always_include_hours: bool = False, decimal


if __name__ == "__main__":
pipeline = FlaxWhisperPipline(checkpoint, dtype=jnp.bfloat16, batch_size=BATCH_SIZE)
pipeline = FlaxWhisperPipeline(checkpoint, dtype=jnp.bfloat16, batch_size=BATCH_SIZE)
stride_length_s = CHUNK_LENGTH_S / 6
chunk_len = round(CHUNK_LENGTH_S * pipeline.feature_extractor.sampling_rate)
stride_left = stride_right = round(stride_length_s * pipeline.feature_extractor.sampling_rate)
Expand Down
2 changes: 1 addition & 1 deletion whisper-jax-tpu.ipynb
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2 changes: 1 addition & 1 deletion whisper_jax/__init__.py
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Expand Up @@ -17,5 +17,5 @@

from .modeling_flax_whisper import FlaxWhisperForConditionalGeneration
from .partitioner import PjitPartitioner
from .pipeline import FlaxWhisperPipline
from .pipeline import FlaxWhisperPipeline
from .train_state import InferenceState
2 changes: 1 addition & 1 deletion whisper_jax/pipeline.py
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Expand Up @@ -54,7 +54,7 @@
)


class FlaxWhisperPipline:
class FlaxWhisperPipeline:
def __init__(
self,
checkpoint="openai/whisper-large-v2",
Expand Down