whisper.cpp/scripts/bench.py
Georgi Gerganov 2e59dced12
whisper : rename binaries + fix install (#2648)
* whisper : rename binaries + fix install

* cont : try to fix ci

* cont : fix emscripten builds
2024-12-21 09:43:49 +02:00

226 lines
6.7 KiB
Python

import os
import subprocess
import re
import csv
import wave
import contextlib
import argparse
# Custom action to handle comma-separated list
class ListAction(argparse.Action):
def __call__(self, parser, namespace, values, option_string=None):
setattr(namespace, self.dest, [int(val) for val in values.split(",")])
parser = argparse.ArgumentParser(description="Benchmark the speech recognition model")
# Define the argument to accept a list
parser.add_argument(
"-t",
"--threads",
dest="threads",
action=ListAction,
default=[4],
help="List of thread counts to benchmark (comma-separated, default: 4)",
)
parser.add_argument(
"-p",
"--processors",
dest="processors",
action=ListAction,
default=[1],
help="List of processor counts to benchmark (comma-separated, default: 1)",
)
parser.add_argument(
"-f",
"--filename",
type=str,
default="./samples/jfk.wav",
help="Relative path of the file to transcribe (default: ./samples/jfk.wav)",
)
# Parse the command line arguments
args = parser.parse_args()
sample_file = args.filename
threads = args.threads
processors = args.processors
# Define the models, threads, and processor counts to benchmark
models = [
"ggml-tiny.en.bin",
"ggml-tiny.bin",
"ggml-base.en.bin",
"ggml-base.bin",
"ggml-small.en.bin",
"ggml-small.bin",
"ggml-medium.en.bin",
"ggml-medium.bin",
"ggml-large-v1.bin",
"ggml-large-v2.bin",
"ggml-large-v3.bin",
"ggml-large-v3-turbo.bin",
]
metal_device = ""
# Initialize a dictionary to hold the results
results = {}
gitHashHeader = "Commit"
modelHeader = "Model"
hardwareHeader = "Hardware"
recordingLengthHeader = "Recording Length (seconds)"
threadHeader = "Thread"
processorCountHeader = "Processor Count"
loadTimeHeader = "Load Time (ms)"
sampleTimeHeader = "Sample Time (ms)"
encodeTimeHeader = "Encode Time (ms)"
decodeTimeHeader = "Decode Time (ms)"
sampleTimePerRunHeader = "Sample Time per Run (ms)"
encodeTimePerRunHeader = "Encode Time per Run (ms)"
decodeTimePerRunHeader = "Decode Time per Run (ms)"
totalTimeHeader = "Total Time (ms)"
def check_file_exists(file: str) -> bool:
return os.path.isfile(file)
def get_git_short_hash() -> str:
try:
return (
subprocess.check_output(["git", "rev-parse", "--short", "HEAD"])
.decode()
.strip()
)
except subprocess.CalledProcessError as e:
return ""
def wav_file_length(file: str = sample_file) -> float:
with contextlib.closing(wave.open(file, "r")) as f:
frames = f.getnframes()
rate = f.getframerate()
duration = frames / float(rate)
return duration
def extract_metrics(output: str, label: str) -> tuple[float, float]:
match = re.search(rf"{label} \s*=\s*(\d+\.\d+)\s*ms\s*/\s*(\d+)\s*runs", output)
time = float(match.group(1)) if match else None
runs = float(match.group(2)) if match else None
return time, runs
def extract_device(output: str) -> str:
match = re.search(r"picking default device: (.*)", output)
device = match.group(1) if match else "Not found"
return device
# Check if the sample file exists
if not check_file_exists(sample_file):
raise FileNotFoundError(f"Sample file {sample_file} not found")
recording_length = wav_file_length()
# Check that all models exist
# Filter out models from list that are not downloaded
filtered_models = []
for model in models:
if check_file_exists(f"models/{model}"):
filtered_models.append(model)
else:
print(f"Model {model} not found, removing from list")
models = filtered_models
# Loop over each combination of parameters
for model in filtered_models:
for thread in threads:
for processor_count in processors:
# Construct the command to run
cmd = f"./build/bin/whisper-cli -m models/{model} -t {thread} -p {processor_count} -f {sample_file}"
# Run the command and get the output
process = subprocess.Popen(
cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT
)
output = ""
while process.poll() is None:
output += process.stdout.read().decode()
# Parse the output
load_time_match = re.search(r"load time\s*=\s*(\d+\.\d+)\s*ms", output)
load_time = float(load_time_match.group(1)) if load_time_match else None
metal_device = extract_device(output)
sample_time, sample_runs = extract_metrics(output, "sample time")
encode_time, encode_runs = extract_metrics(output, "encode time")
decode_time, decode_runs = extract_metrics(output, "decode time")
total_time_match = re.search(r"total time\s*=\s*(\d+\.\d+)\s*ms", output)
total_time = float(total_time_match.group(1)) if total_time_match else None
model_name = model.replace("ggml-", "").replace(".bin", "")
print(
f"Ran model={model_name} threads={thread} processor_count={processor_count}, took {total_time}ms"
)
# Store the times in the results dictionary
results[(model_name, thread, processor_count)] = {
loadTimeHeader: load_time,
sampleTimeHeader: sample_time,
encodeTimeHeader: encode_time,
decodeTimeHeader: decode_time,
sampleTimePerRunHeader: round(sample_time / sample_runs, 2),
encodeTimePerRunHeader: round(encode_time / encode_runs, 2),
decodeTimePerRunHeader: round(decode_time / decode_runs, 2),
totalTimeHeader: total_time,
}
# Write the results to a CSV file
with open("benchmark_results.csv", "w", newline="") as csvfile:
fieldnames = [
gitHashHeader,
modelHeader,
hardwareHeader,
recordingLengthHeader,
threadHeader,
processorCountHeader,
loadTimeHeader,
sampleTimeHeader,
encodeTimeHeader,
decodeTimeHeader,
sampleTimePerRunHeader,
encodeTimePerRunHeader,
decodeTimePerRunHeader,
totalTimeHeader,
]
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
shortHash = get_git_short_hash()
# Sort the results by total time in ascending order
sorted_results = sorted(results.items(), key=lambda x: x[1].get(totalTimeHeader, 0))
for params, times in sorted_results:
row = {
gitHashHeader: shortHash,
modelHeader: params[0],
hardwareHeader: metal_device,
recordingLengthHeader: recording_length,
threadHeader: params[1],
processorCountHeader: params[2],
}
row.update(times)
writer.writerow(row)